Inferring time of infection from field data using dynamic models of antibody decay
Benny Borremans\(^{1,2,3}\)
Riley O Mummah\(^1\)
Angela H Guglielmino\(^1\)
Renee L Galloway\(^4\)
Niel Hens\(^{2,5}\)
K C Prager\(^1\)
James O Lloyd-Smith\(^1\)
\(^1\)Ecology and Evolutionary Biology Department, University of California Los Angeles, United States \(^2\)I-BioStat, Data Science Institute, Hasselt University, Belgium \(^3\)Evolutionary Ecology Group, University of Antwerp, Belgium \(^4\)Centers for Disease Control and Prevention, United States \(^5\)Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Belgium
This document provides code used to generate results and figures
shown in the paper “Inferring time of infection from field data using
dynamic models of antibody decay”.
For more information and details, please see the paper and the
supporting information document.
1 Import and prepare data
observed.data.for.fitting = readRDS("Antibody_decay_data.RDS")
observed.data.for.fitting.incl.neg = readRDS("Antibody_decay_data_incl_neg.RDS")
neg.intervals = observed.data.for.fitting.incl.neg$time[which(observed.data.for.fitting.incl.neg$seroconversion.interval==T)]
kbl(observed.data.for.fitting) %>%
kable_classic(bootstrap_options = c("striped", "hover","condensed"), full_width=F,html_font="Cambria",fixed_thead=T) %>%
scroll_box(height="400px")| Pittag | id | time | titer.pomona | titer.aut | lab |
|---|---|---|---|---|---|
| 00141 | 1 | 0 | 3 | NA | 1 |
| 00141 | 1 | 586 | 2 | NA | 1 |
| 00701 | 2 | 0 | 5 | 6 | 0 |
| 00701 | 2 | 379 | 3 | 3 | 0 |
| 00701 | 2 | 743 | 2 | NA | 0 |
| 00701 | 2 | 1093 | 2 | NA | 0 |
| 00978 | 3 | 0 | 4 | NA | 1 |
| 00978 | 3 | 279 | 3 | NA | 1 |
| 00978 | 3 | 629 | 2 | NA | 1 |
| 00D05 | 4 | 0 | 7 | 11 | 0 |
| 00D05 | 4 | 969 | 3 | 5 | 0 |
| 00D05 | 4 | 1360 | 3 | 5 | 0 |
| 00D05 | 4 | 1854 | 3 | 4 | 0 |
| 00D05 | 4 | 2038 | 3 | 5 | 0 |
| 01334 | 5 | 0 | 6 | NA | 1 |
| 01334 | 5 | 380 | 5 | NA | 1 |
| 01520 | 6 | 0 | 4 | NA | 1 |
| 01520 | 6 | 360 | 4 | NA | 1 |
| 01619 | 7 | 0 | 4 | NA | 1 |
| 01619 | 7 | 352 | 3 | NA | 1 |
| 01619 | 7 | 699 | 3 | NA | 1 |
| 01744 | 8 | 0 | 4 | 5 | 0 |
| 01744 | 8 | 392 | 2 | 3 | 0 |
| 01744 | 8 | 1052 | 2 | 3 | 0 |
| 02107 | 9 | 0 | 7 | 9 | 0 |
| 02107 | 9 | 371 | 7 | 9 | 0 |
| 02107 | 9 | 729 | 3 | 5 | 0 |
| 02107 | 9 | 1092 | 0 | 3 | 0 |
| 03466 | 10 | 0 | 4 | 5 | 0 |
| 03466 | 10 | 340 | 5 | 6 | 0 |
| 03466 | 10 | 694 | 4 | NA | 0 |
| 03662 | 11 | 0 | 4 | 4 | 0 |
| 03662 | 11 | 346 | 3 | NA | 0 |
| 03662 | 11 | 1019 | 2 | NA | 1 |
| 03662 | 11 | 2116 | 1 | NA | 1 |
| 0433A | 12 | 0 | 3 | 5 | 0 |
| 0433A | 12 | 5 | 3 | 4 | 0 |
| 0433A | 12 | 1043 | 1 | 4 | 0 |
| 04471 | 13 | 0 | 2 | 12 | 0 |
| 04703 | 14 | 0 | 7 | 8 | 0 |
| 04703 | 14 | 370 | 4 | 6 | 0 |
| 04703 | 14 | 720 | 3 | 4 | 0 |
| 04703 | 14 | 1072 | 3 | 3 | 0 |
| 04703 | 14 | 1476 | 2 | NA | 1 |
| 04703 | 14 | 1805 | 0 | NA | 1 |
| 04703 | 14 | 2156 | 0 | NA | 1 |
| 04703 | 14 | 2533 | 0 | NA | 1 |
| 04953 | 15 | 0 | 5 | 6 | 0 |
| 04953 | 15 | 265 | 2 | 3 | 0 |
| 04953 | 15 | 647 | 2 | 3 | 0 |
| 04953 | 15 | 739 | 3 | 5 | 0 |
| 04953 | 15 | 1359 | 0 | 3 | 0 |
| 05735 | 16 | 0 | 7 | 9 | 0 |
| 05735 | 16 | 688 | 5 | 6 | 0 |
| 05735 | 16 | 1062 | 3 | NA | 0 |
| 05735 | 16 | 2285 | 2 | NA | 1 |
| 05824 | 17 | 0 | 7 | 8 | 0 |
| 05824 | 17 | 1099 | 2 | NA | 0 |
| 05824 | 17 | 1450 | 1 | NA | 0 |
| 05824 | 17 | 1825 | 2 | NA | 1 |
| 06263 | 18 | 0 | 5 | NA | 1 |
| 06263 | 18 | 406 | 4 | NA | 1 |
| 06436 | 19 | 0 | 3 | 2 | 0 |
| 06436 | 19 | 313 | 4 | 3 | 0 |
| 06436 | 19 | 668 | 1 | 0 | 0 |
| 06436 | 19 | 1060 | 2 | NA | 0 |
| 06441 | 20 | 0 | 5 | NA | 1 |
| 06441 | 20 | 351 | 2 | NA | 1 |
| 06978 | 21 | 0 | 8 | 7 | 0 |
| 06978 | 21 | 449 | 4 | 6 | 0 |
| 06978 | 21 | 736 | 4 | NA | 0 |
| 07127 | 22 | 0 | 3 | 4 | 0 |
| 07127 | 22 | 357 | 3 | NA | 0 |
| 07127 | 22 | 725 | 0 | NA | 0 |
| 07127 | 22 | 1486 | 1 | NA | 1 |
| 07127 | 22 | 2184 | 0 | NA | 1 |
| 07478 | 23 | 0 | 6 | 7 | 0 |
| 07478 | 23 | 578 | 4 | 4 | 0 |
| 07516 | 24 | 0 | 5 | NA | 1 |
| 07516 | 24 | 365 | 4 | NA | 1 |
| 07620 | 25 | 0 | 9 | NA | 1 |
| 07620 | 25 | 351 | 4 | NA | 1 |
| 07906 | 26 | 0 | 8 | NA | 1 |
| 07906 | 26 | 357 | 7 | NA | 1 |
| 07906 | 26 | 733 | 4 | NA | 1 |
| 11709 | 27 | 0 | 7 | NA | 1 |
| 11709 | 27 | 355 | 7 | NA | 1 |
| 11709 | 27 | 735 | 4 | NA | 1 |
| 11726 | 28 | 0 | 4 | 4 | 0 |
| 11726 | 28 | 375 | 5 | 5 | 0 |
| 11726 | 28 | 861 | 4 | NA | 0 |
| 11726 | 28 | 1858 | 2 | NA | 1 |
| 11726 | 28 | 2207 | 1 | NA | 1 |
| 11726 | 28 | 2559 | 1 | NA | 1 |
| 12672 | 29 | 0 | 5 | 5 | 0 |
| 12672 | 29 | 168 | 4 | 4 | 0 |
| 12672 | 29 | 1172 | 4 | 5 | 0 |
| 13062 | 30 | 0 | 3 | 4 | 0 |
| 13062 | 30 | 166 | 4 | 5 | 0 |
| 13062 | 30 | 536 | 3 | NA | 0 |
| 13737 | 31 | 0 | 7 | 8 | 0 |
| 13737 | 31 | 435 | 3 | 4 | 0 |
| 14353 | 32 | 0 | 8 | NA | 1 |
| 14353 | 32 | 356 | 4 | NA | 1 |
| 14732 | 33 | 0 | 7 | 9 | 0 |
| 14732 | 33 | 409 | 3 | 5 | 0 |
| 14732 | 33 | 1113 | 2 | 5 | 0 |
| 14A0D | 34 | 0 | 8 | 9 | 0 |
| 14A0D | 34 | 380 | 7 | 8 | 0 |
| 14A0D | 34 | 757 | 7 | 8 | 0 |
| 14A0D | 34 | 851 | 6 | 8 | 0 |
| 14A0D | 34 | 1124 | 5 | 6 | 0 |
| 14A0D | 34 | 1472 | 5 | 6 | 0 |
| 14A0D | 34 | 1949 | 5 | NA | 1 |
| 15627 | 35 | 0 | 4 | NA | 0 |
| 15627 | 35 | 350 | 2 | NA | 0 |
| 15627 | 35 | 756 | 3 | NA | 1 |
| 15660 | 36 | 0 | 9 | NA | 0 |
| 15660 | 36 | 90 | 8 | NA | 0 |
| 15690 | 37 | 0 | 9 | 9 | 0 |
| 15690 | 37 | 455 | 7 | NA | 1 |
| 15690 | 37 | 726 | 3 | NA | 1 |
| 15690 | 37 | 1092 | 4 | NA | 1 |
| 15690 | 37 | 1462 | 3 | NA | 1 |
| 15718 | 38 | 0 | 8 | NA | 0 |
| 15718 | 38 | 447 | 6 | NA | 1 |
| 15718 | 38 | 762 | 4 | NA | 1 |
| 15718 | 38 | 1084 | 4 | NA | 1 |
| 15718 | 38 | 1477 | 4 | NA | 1 |
| 15738 | 39 | 0 | 4 | NA | 0 |
| 15738 | 39 | 371 | 4 | NA | 0 |
| 15738 | 39 | 753 | 5 | NA | 1 |
| 15738 | 39 | 1903 | 4 | NA | 1 |
| 15748 | 40 | 0 | 6 | NA | 0 |
| 15748 | 40 | 389 | 5 | NA | 0 |
| 15748 | 40 | 765 | 4 | NA | 1 |
| 15748 | 40 | 1120 | 3 | NA | 1 |
| 15748 | 40 | 1293 | 3 | NA | 1 |
| 15748 | 40 | 1482 | 2 | NA | 1 |
| 15755 | 41 | 0 | 3 | NA | 1 |
| 15755 | 41 | 416 | 2 | NA | 1 |
| 15755 | 41 | 763 | 2 | NA | 1 |
| 15765 | 42 | 0 | 4 | 6 | 0 |
| 15765 | 42 | 390 | 1 | 2 | 0 |
| 15765 | 42 | 1053 | 0 | 1 | 0 |
| 15765 | 42 | 1419 | 0 | 0 | 0 |
| 15781 | 43 | 0 | 7 | 8 | 0 |
| 15781 | 43 | 463 | 4 | NA | 1 |
| 15781 | 43 | 831 | 3 | NA | 1 |
| 15803 | 44 | 0 | 5 | NA | 0 |
| 15803 | 44 | 381 | 3 | 6 | 0 |
| 15816 | 45 | 0 | 8 | NA | 0 |
| 15816 | 45 | 1836 | 5 | NA | 1 |
| 16031 | 46 | 0 | 4 | NA | 1 |
| 16031 | 46 | 463 | 3 | NA | 1 |
| 16039 | 47 | 0 | 4 | 4 | 0 |
| 16039 | 47 | 368 | 3 | NA | 0 |
| 16039 | 47 | 601 | 2 | 4 | 0 |
| 16039 | 47 | 685 | 2 | 3 | 0 |
| 16039 | 47 | 737 | 2 | 4 | 0 |
| 16039 | 47 | 1806 | 2 | NA | 1 |
| 16042 | 48 | 0 | 5 | NA | 1 |
| 16042 | 48 | 393 | 3 | NA | 1 |
| 16044 | 49 | 0 | 2 | NA | 0 |
| 16044 | 49 | 390 | 2 | 3 | 0 |
| 16055 | 50 | 0 | 6 | 7 | 0 |
| 16055 | 50 | 760 | 4 | NA | 1 |
| 16055 | 50 | 1482 | 3 | NA | 1 |
| 16072 | 51 | 0 | 3 | NA | 0 |
| 16072 | 51 | 127 | 3 | NA | 0 |
| 16072 | 51 | 1082 | 1 | NA | 1 |
| 16076 | 52 | 0 | 2 | 3 | 0 |
| 16076 | 52 | 345 | 2 | NA | 0 |
| 16081 | 53 | 0 | 7 | NA | 1 |
| 16081 | 53 | 349 | 5 | NA | 1 |
| 16081 | 53 | 729 | 3 | NA | 1 |
| 16082 | 54 | 0 | 6 | 7 | 0 |
| 16082 | 54 | 343 | 6 | NA | 0 |
| 16083 | 55 | 0 | 2 | 3 | 0 |
| 16083 | 55 | 615 | 1 | 3 | 0 |
| 16103 | 56 | 0 | 4 | NA | 0 |
| 16103 | 56 | 102 | 3 | NA | 0 |
| 16103 | 56 | 350 | 4 | 4 | 0 |
| 16103 | 56 | 756 | 3 | NA | 1 |
| 16327 | 57 | 0 | 8 | NA | 1 |
| 16327 | 57 | 357 | 3 | NA | 1 |
| 16327 | 57 | 741 | 3 | NA | 1 |
| 16329 | 58 | 0 | 3 | NA | 1 |
| 16329 | 58 | 189 | 2 | NA | 1 |
| 16329 | 58 | 557 | 0 | NA | 1 |
| 16330 | 59 | 0 | 5 | NA | 1 |
| 16330 | 59 | 380 | 4 | NA | 1 |
| 16343 | 60 | 0 | 3 | NA | 1 |
| 16343 | 60 | 648 | 2 | NA | 1 |
| 16345 | 61 | 0 | 9 | NA | 1 |
| 16345 | 61 | 485 | 0 | NA | 1 |
| 16368 | 62 | 0 | 5 | 6 | 0 |
| 16368 | 62 | 427 | 5 | NA | 1 |
| 16368 | 62 | 1151 | 4 | NA | 1 |
| 16377 | 63 | 0 | 6 | NA | 1 |
| 16377 | 63 | 351 | 5 | NA | 1 |
| 16377 | 63 | 702 | 5 | NA | 1 |
| 16381 | 64 | 0 | 6 | 1 | 0 |
| 16381 | 64 | 323 | 3 | NA | 1 |
| 16381 | 64 | 739 | 1 | NA | 1 |
| 16384 | 65 | 0 | 9 | NA | 1 |
| 16384 | 65 | 396 | 6 | NA | 1 |
| 16385 | 66 | 0 | 9 | NA | 1 |
| 16385 | 66 | 358 | 5 | NA | 1 |
| 16385 | 66 | 740 | 6 | NA | 1 |
| 16397 | 67 | 0 | 9 | NA | 1 |
| 16397 | 67 | 392 | 4 | NA | 1 |
| 16418 | 68 | 0 | 5 | NA | 1 |
| 16418 | 68 | 1130 | 1 | NA | 1 |
| 16424 | 69 | 0 | 3 | NA | 1 |
| 16424 | 69 | 331 | 2 | NA | 1 |
| 16424 | 69 | 678 | 2 | NA | 1 |
| 16424 | 69 | 1072 | 0 | NA | 1 |
| 16431 | 70 | 0 | 9 | NA | 1 |
| 16431 | 70 | 391 | 6 | NA | 1 |
| 16434 | 71 | 0 | 10 | NA | 1 |
| 16434 | 71 | 281 | 5 | NA | 1 |
| 16436 | 72 | 0 | 3 | NA | 0 |
| 16436 | 72 | 245 | 2 | 3 | 0 |
| 16472 | 73 | 0 | 4 | 5 | 0 |
| 16472 | 73 | 449 | 4 | NA | 1 |
| 16476 | 74 | 0 | 7 | NA | 1 |
| 16476 | 74 | 274 | 5 | NA | 1 |
| 16476 | 74 | 638 | 5 | NA | 1 |
| 16476 | 74 | 1006 | 5 | NA | 1 |
| 16508 | 75 | 0 | 5 | NA | 1 |
| 16508 | 75 | 390 | 3 | NA | 1 |
| 16508 | 75 | 748 | 3 | NA | 1 |
| 16520 | 76 | 0 | 6 | NA | 1 |
| 16520 | 76 | 349 | 6 | NA | 1 |
| 16520 | 76 | 727 | 4 | NA | 1 |
| 16523 | 77 | 0 | 7 | NA | 1 |
| 16523 | 77 | 275 | 5 | NA | 1 |
| 16523 | 77 | 638 | 6 | NA | 1 |
| 16955 | 78 | 0 | 7 | 7 | 0 |
| 16955 | 78 | 112 | 6 | 6 | 0 |
| 16955 | 78 | 380 | 5 | NA | 0 |
| 17514 | 79 | 0 | 5 | 9 | 0 |
| 20472 | 80 | 0 | 7 | NA | 1 |
| 20472 | 80 | 368 | 3 | NA | 1 |
| 21214 | 81 | 0 | 5 | 7 | 0 |
| 21214 | 81 | 313 | 3 | 4 | 0 |
| 21285 | 82 | 0 | 6 | 6 | 0 |
| 21285 | 82 | 117 | 5 | 4 | 0 |
| 21308 | 83 | 0 | 7 | NA | 1 |
| 21308 | 83 | 232 | 8 | NA | 1 |
| 21325 | 84 | 0 | 8 | 8 | 0 |
| 21325 | 84 | 388 | 7 | NA | 0 |
| 21433 | 85 | 0 | 5 | 5 | 0 |
| 21433 | 85 | 373 | 2 | NA | 0 |
| 22354 | 86 | 0 | 4 | 4 | 0 |
| 22354 | 86 | 1463 | 2 | NA | 0 |
| 22354 | 86 | 1856 | 2 | 1 | 0 |
| 23147 | 87 | 0 | 7 | 6 | 0 |
| 23147 | 87 | 52 | 6 | 6 | 0 |
| 23147 | 87 | 409 | 5 | NA | 0 |
| 23147 | 87 | 773 | 4 | 5 | 0 |
| 23212 | 88 | 0 | 7 | 7 | 0 |
| 23212 | 88 | 373 | 3 | 3 | 0 |
| 23212 | 88 | 401 | 3 | 4 | 0 |
| 23212 | 88 | 749 | 3 | 3 | 0 |
| 23212 | 88 | 1158 | 2 | NA | 1 |
| 23727 | 89 | 0 | 7 | 7 | 0 |
| 23727 | 89 | 395 | 3 | 5 | 0 |
| 23727 | 89 | 747 | 3 | 4 | 0 |
| 23727 | 89 | 1193 | 3 | NA | 1 |
| 23777 | 90 | 0 | 8 | NA | 1 |
| 23777 | 90 | 280 | 5 | NA | 1 |
| 23795 | 91 | 0 | 10 | NA | 0 |
| 23795 | 91 | 360 | 6 | NA | 0 |
| 23797 | 92 | 0 | 6 | NA | 1 |
| 23797 | 92 | 1007 | 4 | NA | 1 |
| 23806 | 93 | 0 | 7 | 7 | 0 |
| 23806 | 93 | 456 | 6 | NA | 1 |
| 23806 | 93 | 725 | 5 | NA | 1 |
| 23806 | 93 | 1090 | 5 | NA | 1 |
| 23813 | 94 | 0 | 7 | NA | 1 |
| 23813 | 94 | 276 | 5 | NA | 1 |
| 23813 | 94 | 626 | 5 | NA | 1 |
| 23813 | 94 | 1004 | 4 | NA | 1 |
| 23816 | 95 | 0 | 8 | 9 | 0 |
| 23816 | 95 | 891 | 3 | NA | 1 |
| 23823 | 96 | 0 | 2 | NA | 1 |
| 23823 | 96 | 1096 | 2 | NA | 1 |
| 23839 | 97 | 0 | 9 | NA | 1 |
| 23839 | 97 | 281 | 5 | NA | 1 |
| 23839 | 97 | 1008 | 3 | NA | 1 |
| 23845 | 98 | 0 | 8 | NA | 1 |
| 23845 | 98 | 332 | 4 | NA | 1 |
| 23845 | 98 | 1057 | 4 | NA | 1 |
| 23850 | 99 | 0 | 8 | 11 | 0 |
| 23850 | 99 | 89 | 8 | 10 | 0 |
| 23983 | 100 | 0 | 3 | NA | 1 |
| 23983 | 100 | 698 | 1 | NA | 1 |
| 23988 | 101 | 0 | 7 | NA | 1 |
| 23988 | 101 | 349 | 6 | NA | 1 |
| 23988 | 101 | 727 | 5 | NA | 1 |
| 24003 | 102 | 0 | 7 | NA | 1 |
| 24003 | 102 | 350 | 5 | NA | 1 |
| 24003 | 102 | 466 | 5 | NA | 1 |
| 24003 | 102 | 730 | 4 | NA | 1 |
| 24024 | 103 | 0 | 7 | NA | 1 |
| 24024 | 103 | 351 | 6 | NA | 1 |
| 24024 | 103 | 700 | 3 | NA | 1 |
| 24038 | 104 | 0 | 6 | NA | 1 |
| 24038 | 104 | 360 | 7 | NA | 1 |
| 24045 | 105 | 0 | 7 | NA | 1 |
| 24045 | 105 | 331 | 4 | NA | 1 |
| 24045 | 105 | 682 | 4 | NA | 1 |
| 24049 | 106 | 0 | 7 | NA | 1 |
| 24049 | 106 | 346 | 5 | NA | 1 |
| 24049 | 106 | 700 | 3 | NA | 1 |
| 24051 | 107 | 0 | 2 | NA | 0 |
| 24051 | 107 | 613 | 3 | NA | 1 |
| 24051 | 107 | 965 | 4 | NA | 1 |
| 24051 | 107 | 1314 | 3 | NA | 1 |
| 24068 | 108 | 0 | 9 | NA | 1 |
| 24068 | 108 | 1074 | 3 | NA | 1 |
| 24072 | 109 | 0 | 8 | 7 | 0 |
| 24072 | 109 | 797 | 2 | NA | 1 |
| 24085 | 110 | 0 | 7 | NA | 1 |
| 24085 | 110 | 387 | 4 | NA | 1 |
| 24105 | 111 | 0 | 9 | NA | 1 |
| 24105 | 111 | 273 | 7 | NA | 1 |
| 24121 | 112 | 0 | 9 | NA | 1 |
| 24121 | 112 | 392 | 3 | NA | 1 |
| 24124 | 113 | 0 | 7 | NA | 1 |
| 24127 | 114 | 0 | 7 | NA | 1 |
| 24127 | 114 | 331 | 3 | NA | 1 |
| 24127 | 114 | 679 | 3 | NA | 1 |
| 24127 | 114 | 1072 | 1 | NA | 1 |
| 24129 | 115 | 0 | 7 | NA | 1 |
| 24129 | 115 | 349 | 8 | NA | 1 |
| 24129 | 115 | 727 | 6 | NA | 1 |
| 24148 | 116 | 0 | 7 | NA | 1 |
| 24148 | 116 | 330 | 3 | NA | 1 |
| 24156 | 117 | 0 | 8 | NA | 1 |
| 24156 | 117 | 330 | 5 | NA | 1 |
| 24156 | 117 | 684 | 5 | NA | 1 |
| 24156 | 117 | 1075 | 5 | NA | 1 |
| 24162 | 118 | 0 | 6 | NA | 1 |
| 24162 | 118 | 321 | 5 | NA | 1 |
| 24269 | 119 | 0 | 4 | 5 | 0 |
| 24269 | 119 | 105 | 4 | 6 | 0 |
| 24269 | 119 | 304 | 3 | 5 | 0 |
| 24269 | 119 | 346 | 3 | 4 | 0 |
| 24269 | 119 | 805 | 4 | NA | 1 |
| 24269 | 119 | 1106 | 2 | NA | 1 |
| 24375 | 120 | 0 | 6 | NA | 1 |
| 24375 | 120 | 388 | 5 | NA | 1 |
| 24409 | 121 | 0 | 9 | NA | 1 |
| 24409 | 121 | 357 | 4 | NA | 1 |
| 24440 | 122 | 0 | 8 | NA | 1 |
| 24440 | 122 | 352 | 7 | NA | 1 |
| 24440 | 122 | 738 | 5 | NA | 1 |
| 24912 | 123 | 0 | 6 | NA | 1 |
| 24912 | 123 | 393 | 4 | NA | 1 |
| 24925 | 124 | 0 | 3 | 5 | 0 |
| 24925 | 124 | 484 | 2 | NA | 0 |
| 2543F | 125 | 0 | 4 | 3 | 0 |
| 2543F | 125 | 1375 | 1 | 0 | 0 |
| 25E35 | 126 | 0 | 6 | 7 | 0 |
| 25E35 | 126 | 823 | 4 | 4 | 0 |
| 26070 | 127 | 0 | 6 | NA | 0 |
| 26070 | 127 | 388 | 3 | 6 | 0 |
| 26070 | 127 | 766 | 4 | NA | 1 |
| 26070 | 127 | 1121 | 3 | NA | 1 |
| 26070 | 127 | 1482 | 3 | NA | 1 |
| 26070 | 127 | 1848 | 2 | NA | 1 |
| 26079 | 128 | 0 | 7 | 7 | 0 |
| 26079 | 128 | 480 | 3 | NA | 0 |
| 26079 | 128 | 1393 | 2 | NA | 1 |
| 26082 | 129 | 0 | 1 | 2 | 0 |
| 26082 | 129 | 162 | 1 | 3 | 0 |
| 26190 | 130 | 0 | 8 | NA | 0 |
| 26190 | 130 | 351 | 4 | 6 | 0 |
| 26190 | 130 | 826 | 4 | NA | 1 |
| 26190 | 130 | 1106 | 3 | NA | 1 |
| 26190 | 130 | 1457 | 3 | NA | 1 |
| 26190 | 130 | 1834 | 3 | NA | 1 |
| 26195 | 131 | 0 | 8 | 8 | 0 |
| 26195 | 131 | 477 | 7 | NA | 1 |
| 26195 | 131 | 757 | 5 | NA | 1 |
| 26195 | 131 | 1109 | 6 | NA | 1 |
| 26195 | 131 | 1486 | 3 | NA | 1 |
| 26196 | 132 | 0 | 3 | NA | 0 |
| 26196 | 132 | 382 | 3 | 2 | 0 |
| 26196 | 132 | 1858 | 1 | NA | 1 |
| 26225 | 133 | 0 | 5 | 7 | 0 |
| 26225 | 133 | 362 | 3 | NA | 0 |
| 26231 | 134 | 0 | 5 | 6 | 0 |
| 26231 | 134 | 364 | 3 | NA | 0 |
| 31439 | 135 | 0 | 4 | 5 | 0 |
| 31439 | 135 | 785 | 2 | 3 | 0 |
| 31439 | 135 | 1134 | 1 | 3 | 0 |
| 31439 | 135 | 1486 | 0 | 2 | 0 |
| 32E5F | 136 | 0 | 3 | 4 | 0 |
| 32E5F | 136 | 271 | 2 | 3 | 0 |
| 32E5F | 136 | 615 | 1 | 2 | 0 |
| 32E5F | 136 | 1087 | 1 | 2 | 0 |
| 32E5F | 136 | 1777 | 1 | NA | 0 |
| 3336E | 137 | 0 | 3 | 5 | 1 |
| 3336E | 137 | 157 | 1 | 2 | 0 |
| 3336E | 137 | 1130 | 0 | 0 | 0 |
| 3336E | 137 | 1481 | 0 | 0 | 0 |
| 3336E | 137 | 1889 | 0 | NA | 1 |
| 3360A | 138 | 0 | 4 | 6 | 0 |
| 3360A | 138 | 366 | 2 | 3 | 0 |
| 3360A | 138 | 391 | 2 | 4 | 0 |
| 33816 | 139 | 0 | 3 | NA | 1 |
| 33816 | 139 | 730 | 2 | NA | 1 |
| 33B58 | 140 | 0 | 7 | 8 | 0 |
| 33B58 | 140 | 1114 | 5 | 5 | 0 |
| 33B58 | 140 | 1504 | 3 | NA | 0 |
| 35252 | 141 | 0 | 3 | 4 | 0 |
| 35252 | 141 | 1054 | 1 | NA | 0 |
| 3581A | 142 | 0 | 6 | 5 | 0 |
| 3581A | 142 | 738 | 5 | 6 | 0 |
| 3604E | 143 | 0 | 4 | 4 | 0 |
| 3604E | 143 | 623 | 4 | 3 | 0 |
| 3604E | 143 | 979 | 2 | NA | 0 |
| 3604E | 143 | 1109 | 2 | NA | 0 |
| 3604E | 143 | 1345 | 3 | 3 | 0 |
| 36401 | 144 | 0 | 5 | 6 | 0 |
| 36401 | 144 | 57 | 6 | 6 | 0 |
| 36417 | 145 | 0 | 4 | 4 | 0 |
| 36417 | 145 | 361 | 3 | 3 | 0 |
| 36417 | 145 | 714 | 2 | 4 | 0 |
| 36417 | 145 | 1077 | 2 | 2 | 0 |
| 36417 | 145 | 1768 | 1 | 2 | 0 |
| 36417 | 145 | 1881 | 1 | 2 | 0 |
| 36813 | 146 | 0 | 5 | NA | 1 |
| 36813 | 146 | 374 | 5 | NA | 1 |
| 37468 | 147 | 0 | 3 | 4 | 0 |
| 37468 | 147 | 105 | 3 | 3 | 0 |
| 37468 | 147 | 486 | 2 | NA | 0 |
| 37471 | 148 | 0 | 7 | NA | 0 |
| 37471 | 148 | 353 | 3 | 4 | 0 |
| 37510 | 149 | 0 | 7 | 8 | 0 |
| 37510 | 149 | 351 | 5 | NA | 0 |
| 37535 | 150 | 0 | 5 | 4 | 0 |
| 37535 | 150 | 320 | 5 | NA | 0 |
| 37539 | 151 | 0 | 8 | 9 | 0 |
| 37539 | 151 | 346 | 6 | 8 | 0 |
| 37539 | 151 | 737 | 3 | 6 | 0 |
| 37539 | 151 | 1090 | 3 | NA | 1 |
| 37539 | 151 | 1495 | 3 | NA | 1 |
| 37539 | 151 | 1836 | 3 | NA | 1 |
| 37541 | 152 | 0 | 6 | 8 | 0 |
| 37541 | 152 | 332 | 5 | NA | 0 |
| 37541 | 152 | 500 | 5 | NA | 0 |
| 37544 | 153 | 0 | 4 | 4 | 0 |
| 37544 | 153 | 449 | 3 | NA | 1 |
| 37544 | 153 | 763 | 2 | NA | 1 |
| 37544 | 153 | 1087 | 3 | NA | 1 |
| 37544 | 153 | 1478 | 0 | NA | 1 |
| 37572 | 154 | 0 | 4 | 5 | 0 |
| 37572 | 154 | 354 | 3 | NA | 0 |
| 37572 | 154 | 744 | 2 | 3 | 0 |
| 37572 | 154 | 1094 | 3 | NA | 1 |
| 37572 | 154 | 1487 | 1 | NA | 1 |
| 37572 | 154 | 1843 | 2 | NA | 1 |
| 37572 | 154 | 2225 | 0 | NA | 1 |
| 37599 | 155 | 0 | 3 | 3 | 0 |
| 37599 | 155 | 112 | 3 | 4 | 0 |
| 37599 | 155 | 389 | 1 | 2 | 0 |
| 37599 | 155 | 738 | 2 | NA | 1 |
| 37599 | 155 | 1142 | 1 | NA | 1 |
| 37599 | 155 | 1940 | 0 | NA | 1 |
| 37630 | 156 | 0 | 5 | 4 | 0 |
| 37630 | 156 | 348 | 3 | NA | 1 |
| 37630 | 156 | 750 | 2 | NA | 1 |
| 37630 | 156 | 1096 | 2 | NA | 1 |
| 37646 | 157 | 0 | 7 | NA | 0 |
| 37646 | 157 | 349 | 6 | 8 | 0 |
| 37646 | 157 | 461 | 5 | 6 | 0 |
| 38958 | 158 | 0 | 5 | 6 | 0 |
| 38958 | 158 | 105 | 4 | 5 | 0 |
| 38958 | 158 | 356 | 5 | NA | 0 |
| 38958 | 158 | 725 | 3 | 4 | 0 |
| 38976 | 159 | 0 | 4 | 4 | 0 |
| 38976 | 159 | 102 | 3 | 4 | 0 |
| 38976 | 159 | 362 | 2 | NA | 0 |
| 38976 | 159 | 777 | 1 | 2 | 0 |
| 38976 | 159 | 1758 | 0 | NA | 1 |
| 38981 | 160 | 0 | 7 | 7 | 0 |
| 38981 | 160 | 532 | 3 | NA | 0 |
| 39013 | 161 | 0 | 7 | 8 | 0 |
| 39013 | 161 | 1635 | 2 | NA | 1 |
| 39013 | 161 | 2346 | 2 | NA | 1 |
| 39014 | 162 | 0 | 6 | 7 | 0 |
| 39014 | 162 | 410 | 5 | NA | 1 |
| 39014 | 162 | 737 | 2 | NA | 1 |
| 39031 | 163 | 0 | 8 | 9 | 0 |
| 39031 | 163 | 321 | 4 | 6 | 0 |
| 39031 | 163 | 712 | 2 | 4 | 0 |
| 39031 | 163 | 1063 | 2 | NA | 1 |
| 39031 | 163 | 1814 | 1 | NA | 1 |
| 39037 | 164 | 0 | 6 | 7 | 0 |
| 39037 | 164 | 366 | 2 | 4 | 0 |
| 39037 | 164 | 533 | 2 | 3 | 0 |
| 39037 | 164 | 1283 | 1 | NA | 1 |
| 39045 | 165 | 0 | 5 | 5 | 0 |
| 39045 | 165 | 318 | 5 | NA | 0 |
| 39045 | 165 | 323 | 4 | NA | 0 |
| 39045 | 165 | 713 | 3 | 3 | 0 |
| 39045 | 165 | 1062 | 4 | NA | 1 |
| 39430 | 166 | 0 | 12 | NA | 0 |
| 39430 | 166 | 97 | 8 | NA | 0 |
| 39430 | 166 | 360 | 4 | NA | 0 |
| 39432 | 167 | 0 | 6 | NA | 0 |
| 39432 | 167 | 349 | 5 | NA | 0 |
| 39432 | 167 | 716 | 4 | 6 | 0 |
| 39432 | 167 | 1169 | 4 | NA | 1 |
| 39432 | 167 | 1447 | 4 | NA | 1 |
| 39432 | 167 | 1808 | 3 | NA | 1 |
| 39454 | 168 | 0 | 6 | NA | 0 |
| 39454 | 168 | 113 | 5 | 4 | 0 |
| 39454 | 168 | 379 | 5 | 3 | 0 |
| 39454 | 168 | 728 | 3 | 2 | 0 |
| 41214 | 169 | 0 | 9 | 10 | 0 |
| 41214 | 169 | 400 | 5 | 7 | 0 |
| 41214 | 169 | 1157 | 4 | NA | 0 |
| 4151F | 170 | 0 | 6 | 10 | 0 |
| 4151F | 170 | 759 | 2 | 4 | 0 |
| 4151F | 170 | 1463 | 1 | NA | 0 |
| 4151F | 170 | 1811 | 1 | NA | 0 |
| 4151F | 170 | 1841 | 1 | NA | 0 |
| 4151F | 170 | 1989 | 2 | 4 | 1 |
| 41795 | 171 | 0 | 2 | 2 | 0 |
| 41795 | 171 | 378 | 2 | 3 | 0 |
| 41795 | 171 | 744 | 0 | 1 | 0 |
| 41795 | 171 | 1096 | 1 | 2 | 0 |
| 41795 | 171 | 1571 | 1 | NA | 1 |
| 41795 | 171 | 1853 | 0 | NA | 1 |
| 41795 | 171 | 2576 | 0 | NA | 1 |
| 42558 | 172 | 0 | 7 | 9 | 0 |
| 42558 | 172 | 315 | 5 | 7 | 0 |
| 42558 | 172 | 1861 | 4 | NA | 0 |
| 43388 | 173 | 0 | 9 | 9 | 0 |
| 43388 | 173 | 1350 | 3 | NA | 1 |
| 43929 | 174 | 0 | 4 | 6 | 0 |
| 43929 | 174 | 377 | 2 | 2 | 0 |
| 43929 | 174 | 765 | 1 | 2 | 0 |
| 4466C | 175 | 0 | 6 | 7 | 0 |
| 4466C | 175 | 414 | 3 | 5 | 0 |
| 4466C | 175 | 786 | 4 | 5 | 0 |
| 4466C | 175 | 1137 | 3 | 4 | 0 |
| 4466C | 175 | 1138 | 3 | 4 | 0 |
| 4466C | 175 | 1488 | 4 | 4 | 1 |
| 4466C | 175 | 1890 | 5 | NA | 1 |
| 4466C | 175 | 2220 | 0 | NA | 1 |
| 46127 | 176 | 0 | 7 | 8 | 1 |
| 46127 | 176 | 577 | 8 | NA | 0 |
| 46127 | 176 | 942 | 5 | NA | 0 |
| 46621 | 177 | 0 | 8 | 8 | 0 |
| 46621 | 177 | 54 | 8 | 10 | 0 |
| 46621 | 177 | 1424 | 2 | NA | 1 |
| 46766 | 178 | 0 | 6 | NA | 0 |
| 46766 | 178 | 750 | 4 | 6 | 0 |
| 47B58 | 179 | 0 | 4 | 6 | 0 |
| 47B58 | 179 | 650 | 2 | 3 | 0 |
| 47B58 | 179 | 1380 | 0 | 2 | 0 |
| 50218 | 180 | 0 | 3 | 4 | 0 |
| 50218 | 180 | 350 | 2 | 2 | 0 |
| 50218 | 180 | 1010 | 2 | NA | 0 |
| 50218 | 180 | 1378 | 2 | NA | 0 |
| 50218 | 180 | 1507 | 3 | NA | 0 |
| 50473 | 181 | 0 | 6 | NA | 0 |
| 50473 | 181 | 161 | 4 | NA | 0 |
| 50473 | 181 | 371 | 3 | NA | 0 |
| 50E01 | 182 | 0 | 6 | 7 | 0 |
| 50E01 | 182 | 984 | 3 | 3 | 0 |
| 50E01 | 182 | 1338 | 3 | 4 | 0 |
| 50E01 | 182 | 2534 | 1 | NA | 0 |
| 51A77 | 183 | 0 | 7 | 7 | 0 |
| 51A77 | 183 | 224 | 7 | 8 | 0 |
| 51A77 | 183 | 1694 | 5 | NA | 0 |
| 52462 | 184 | 0 | 9 | NA | 1 |
| 52462 | 184 | 71 | 8 | NA | 1 |
| 52858 | 185 | 0 | 4 | 6 | 0 |
| 53002 | 186 | 0 | 1 | 3 | 0 |
| 53002 | 186 | 370 | 0 | NA | 0 |
| 53002 | 186 | 723 | 1 | NA | 0 |
| 53002 | 186 | 2549 | 0 | NA | 1 |
| 54516 | 187 | 0 | 6 | 5 | 0 |
| 54516 | 187 | 206 | 5 | 5 | 0 |
| 54516 | 187 | 1555 | 3 | NA | 0 |
| 55860 | 188 | 0 | 5 | 6 | 0 |
| 55860 | 188 | 382 | 3 | 3 | 0 |
| 55860 | 188 | 768 | 3 | 4 | 0 |
| 55860 | 188 | 1114 | 2 | NA | 0 |
| 55860 | 188 | 1143 | 2 | NA | 0 |
| 55860 | 188 | 1858 | 3 | 3 | 0 |
| 56111 | 189 | 0 | 6 | 8 | 0 |
| 56111 | 189 | 614 | 3 | NA | 1 |
| 56141 | 190 | 0 | 5 | 4 | 0 |
| 56141 | 190 | 303 | 4 | 3 | 0 |
| 56141 | 190 | 898 | 3 | 2 | 0 |
| 56141 | 190 | 1262 | 3 | 3 | 0 |
| 56141 | 190 | 1620 | 2 | 2 | 0 |
| 56141 | 190 | 2407 | 1 | NA | 0 |
| 56141 | 190 | 2756 | 1 | 0 | 0 |
| 56141 | 190 | 3838 | 0 | NA | 1 |
| 58646 | 191 | 0 | 6 | 7 | 0 |
| 58646 | 191 | 350 | 4 | 5 | 0 |
| 58646 | 191 | 742 | 3 | 5 | 0 |
| 58646 | 191 | 1114 | 4 | NA | 1 |
| 58646 | 191 | 1472 | 2 | NA | 1 |
| 58646 | 191 | 1833 | 1 | NA | 1 |
| 58646 | 191 | 2199 | 1 | NA | 1 |
| 58709 | 192 | 0 | 8 | NA | 1 |
| 58709 | 192 | 351 | 6 | NA | 1 |
| 59147 | 193 | 0 | 7 | NA | 0 |
| 59147 | 193 | 349 | 5 | NA | 0 |
| 59843 | 194 | 0 | 8 | 8 | 0 |
| 59843 | 194 | 350 | 3 | 4 | 0 |
| 59843 | 194 | 705 | 1 | 2 | 0 |
| 59843 | 194 | 1149 | 1 | NA | 1 |
| 59843 | 194 | 1467 | 0 | NA | 1 |
| 60122 | 195 | 0 | 6 | NA | 1 |
| 60122 | 195 | 70 | 5 | NA | 1 |
| 62159 | 196 | 0 | 1 | 3 | 0 |
| 62159 | 196 | 740 | 1 | 2 | 1 |
| 62159 | 196 | 1090 | 0 | 1 | 0 |
| 62159 | 196 | 1496 | 1 | NA | 1 |
| 62159 | 196 | 1823 | 1 | NA | 1 |
| 62159 | 196 | 2173 | 0 | NA | 1 |
| 62159 | 196 | 2567 | 0 | NA | 1 |
| 62447 | 197 | 0 | 6 | 7 | 0 |
| 62447 | 197 | 413 | 3 | 5 | 0 |
| 62447 | 197 | 786 | 4 | NA | 0 |
| 63653 | 198 | 0 | 6 | 6 | 0 |
| 63653 | 198 | 358 | 3 | 3 | 0 |
| 63653 | 198 | 752 | 3 | 4 | 0 |
| 64939 | 199 | 0 | 6 | 8 | 0 |
| 64939 | 199 | 206 | 4 | 5 | 0 |
| 64939 | 199 | 403 | 4 | 3 | 0 |
| 64939 | 199 | 738 | 2 | 3 | 0 |
| 64939 | 199 | 1478 | 0 | NA | 0 |
| 65143 | 200 | 0 | 3 | 4 | 0 |
| 65143 | 200 | 324 | 3 | 2 | 0 |
| 65143 | 200 | 349 | 3 | 2 | 0 |
| 65143 | 200 | 351 | 3 | 2 | 0 |
| 65143 | 200 | 1380 | 2 | NA | 0 |
| 66F7D | 201 | 0 | 5 | 7 | 0 |
| 66F7D | 201 | 321 | 4 | 5 | 0 |
| 66F7D | 201 | 678 | 3 | 4 | 0 |
| 66F7D | 201 | 1464 | 2 | NA | 0 |
| 66F7D | 201 | 1814 | 3 | NA | 0 |
| 66F7D | 201 | 3051 | 2 | NA | 1 |
| 70486 | 202 | 0 | 9 | NA | 0 |
| 70486 | 202 | 380 | 4 | NA | 0 |
| 70725 | 203 | 0 | 2 | 3 | 0 |
| 70725 | 203 | 781 | 2 | NA | 0 |
| 70725 | 203 | 1693 | 2 | NA | 1 |
| 71112 | 204 | 0 | 6 | 7 | 0 |
| 71112 | 204 | 728 | 6 | 6 | 0 |
| 71112 | 204 | 1105 | 4 | NA | 0 |
| 71353 | 205 | 0 | 7 | 7 | 0 |
| 71353 | 205 | 178 | 0 | 0 | 0 |
| 72320 | 206 | 0 | 8 | 11 | 0 |
| 72320 | 206 | 345 | 4 | 6 | 0 |
| 72320 | 206 | 354 | 4 | 6 | 0 |
| 72320 | 206 | 740 | 4 | 5 | 0 |
| 72320 | 206 | 1113 | 4 | NA | 1 |
| 72320 | 206 | 1470 | 3 | NA | 1 |
| 72320 | 206 | 1834 | 3 | NA | 1 |
| 72320 | 206 | 2200 | 3 | NA | 1 |
| 72507 | 207 | 0 | 7 | 8 | 0 |
| 72507 | 207 | 359 | 5 | 8 | 0 |
| 73050 | 208 | 0 | 4 | NA | 0 |
| 73050 | 208 | 367 | 2 | NA | 0 |
| 73655 | 209 | 0 | 7 | NA | 1 |
| 73655 | 209 | 74 | 4 | NA | 1 |
| 7366A | 210 | 0 | 4 | 5 | 0 |
| 7366A | 210 | 687 | 3 | NA | 0 |
| 7366A | 210 | 821 | 3 | NA | 0 |
| 7366A | 210 | 1554 | 1 | NA | 0 |
| 74770 | 211 | 0 | 7 | NA | 0 |
| 74770 | 211 | 390 | 4 | NA | 0 |
| 74770 | 211 | 1140 | 2 | NA | 1 |
| 74770 | 211 | 1491 | 1 | NA | 1 |
| 7582C | 212 | 0 | 4 | 6 | 0 |
| 7582C | 212 | 579 | 3 | NA | 0 |
| 7582C | 212 | 942 | 3 | NA | 0 |
| 7634C | 213 | 0 | 5 | 5 | 0 |
| 7634C | 213 | 695 | 4 | 5 | 0 |
| 77067 | 214 | 0 | 6 | 6 | 0 |
| 77067 | 214 | 342 | 3 | 2 | 0 |
| 77067 | 214 | 693 | 2 | 2 | 0 |
| 77067 | 214 | 1084 | 0 | 0 | 0 |
| 77067 | 214 | 1434 | 1 | NA | 1 |
| 77067 | 214 | 1825 | 0 | NA | 1 |
| 77067 | 214 | 2183 | 0 | NA | 1 |
| 77067 | 214 | 2565 | 0 | NA | 1 |
| 77808 | 215 | 0 | 8 | NA | 0 |
| 77808 | 215 | 376 | 5 | NA | 0 |
| 77808 | 215 | 1496 | 3 | NA | 1 |
| 78089 | 216 | 0 | 5 | 5 | 0 |
| 78089 | 216 | 340 | 4 | 3 | 0 |
| 78089 | 216 | 688 | 2 | 3 | 0 |
| 78089 | 216 | 1081 | 1 | 2 | 0 |
| 78483 | 217 | 0 | 8 | 6 | 0 |
| 78483 | 217 | 739 | 3 | NA | 0 |
| 78483 | 217 | 842 | 4 | NA | 0 |
| 78483 | 217 | 1090 | 3 | NA | 0 |
| 78483 | 217 | 1565 | 4 | NA | 1 |
| 78624 | 218 | 0 | 6 | 9 | 0 |
| 78624 | 218 | 325 | 5 | 5 | 0 |
| 78624 | 218 | 445 | 5 | 5 | 0 |
| 78624 | 218 | 723 | 4 | 5 | 0 |
| 78624 | 218 | 1089 | 3 | 4 | 0 |
| 78624 | 218 | 2182 | 1 | NA | 1 |
| 78746 | 219 | 0 | 7 | NA | 0 |
| 78746 | 219 | 51 | 7 | NA | 0 |
| 78746 | 219 | 401 | 3 | 3 | 0 |
| 78746 | 219 | 751 | 0 | 0 | 0 |
| 78746 | 219 | 1834 | 1 | NA | 1 |
| 79812 | 220 | 0 | 8 | NA | 1 |
| 79812 | 220 | 350 | 7 | NA | 1 |
| 79839 | 221 | 0 | 4 | NA | 1 |
| 79839 | 221 | 414 | 4 | NA | 1 |
| 79871 | 222 | 0 | 7 | NA | 1 |
| 79871 | 222 | 349 | 4 | NA | 1 |
| 79968 | 223 | 0 | 7 | NA | 1 |
| 79968 | 223 | 378 | 3 | NA | 1 |
| 79987 | 224 | 0 | 6 | NA | 1 |
| 79987 | 224 | 347 | 3 | NA | 1 |
| 80010 | 225 | 0 | 4 | 5 | 0 |
| 80010 | 225 | 784 | 4 | NA | 0 |
| 80043 | 226 | 0 | 3 | NA | 1 |
| 80043 | 226 | 381 | 1 | NA | 1 |
| 80157 | 227 | 0 | 8 | NA | 1 |
| 80157 | 227 | 377 | 7 | NA | 1 |
| 80221 | 228 | 0 | 8 | NA | 1 |
| 80221 | 228 | 348 | 3 | NA | 1 |
| 80337 | 229 | 0 | 6 | NA | 1 |
| 80337 | 229 | 413 | 5 | NA | 1 |
| 80450 | 230 | 0 | 6 | 8 | 0 |
| 80450 | 230 | 370 | 7 | NA | 0 |
| 83948 | 231 | 0 | 7 | NA | 1 |
| 83948 | 231 | 223 | 7 | NA | 1 |
| 84001 | 232 | 0 | 7 | 7 | 0 |
| 84001 | 232 | 331 | 2 | 4 | 0 |
| 84001 | 232 | 682 | 1 | 3 | 0 |
| 84001 | 232 | 1027 | 0 | 2 | 0 |
| 84596 | 233 | 0 | 4 | 4 | 0 |
| 84596 | 233 | 426 | 3 | 2 | 0 |
| 84596 | 233 | 683 | 4 | NA | 0 |
| 84976 | 234 | 0 | 3 | 4 | 0 |
| 84976 | 234 | 351 | 2 | NA | 0 |
| 84976 | 234 | 701 | 1 | 2 | 0 |
| 84976 | 234 | 1091 | 0 | 0 | 0 |
| 84976 | 234 | 1444 | 1 | NA | 1 |
| 84976 | 234 | 2135 | 0 | NA | 1 |
| 84976 | 234 | 2571 | 0 | NA | 1 |
| 84D5D | 235 | 0 | 7 | 8 | 0 |
| 84D5D | 235 | 394 | 5 | 7 | 0 |
| 84D5D | 235 | 555 | 6 | 7 | 0 |
| 84D5D | 235 | 784 | 6 | NA | 0 |
| 84D5D | 235 | 1134 | 4 | NA | 0 |
| 85498 | 236 | 0 | 5 | 6 | 0 |
| 85498 | 236 | 320 | 3 | NA | 0 |
| 85498 | 236 | 500 | 3 | NA | 0 |
| 85498 | 236 | 730 | 2 | NA | 0 |
| 85589 | 237 | 0 | 6 | 7 | 0 |
| 85589 | 237 | 728 | 6 | NA | 0 |
| 85707 | 238 | 0 | 4 | 3 | 0 |
| 85707 | 238 | 502 | 3 | NA | 0 |
| 85730 | 239 | 0 | 9 | 10 | 0 |
| 85730 | 239 | 735 | 4 | NA | 0 |
| 85730 | 239 | 1093 | 3 | NA | 0 |
| 86034 | 240 | 0 | 2 | NA | 1 |
| 86034 | 240 | 301 | 0 | NA | 1 |
| 86076 | 241 | 0 | 11 | 10 | 0 |
| 86076 | 241 | 239 | 7 | 8 | 0 |
| 86076 | 241 | 554 | 6 | 7 | 0 |
| 86076 | 241 | 605 | 5 | 7 | 0 |
| 86076 | 241 | 1329 | 4 | 5 | 0 |
| 86335 | 242 | 0 | 4 | 4 | 0 |
| 86335 | 242 | 374 | 4 | NA | 0 |
| 86335 | 242 | 689 | 3 | NA | 0 |
| 86335 | 242 | 730 | 4 | NA | 0 |
| 86426 | 243 | 0 | 4 | 4 | 0 |
| 86426 | 243 | 335 | 2 | 2 | 0 |
| 86426 | 243 | 709 | 0 | 1 | 0 |
| 86426 | 243 | 1451 | 0 | 0 | 0 |
| 86426 | 243 | 1827 | 0 | NA | 1 |
| 86426 | 243 | 2179 | 0 | NA | 1 |
| 86875 | 244 | 0 | 6 | NA | 0 |
| 86875 | 244 | 362 | 6 | NA | 0 |
| 86875 | 244 | 712 | 5 | NA | 0 |
| 86885 | 245 | 0 | 8 | 0 | 0 |
| 86885 | 245 | 765 | 4 | 5 | 0 |
| 86885 | 245 | 822 | 4 | 5 | 0 |
| 86885 | 245 | 1116 | 4 | 4 | 0 |
| 86885 | 245 | 1490 | 4 | NA | 1 |
| 86885 | 245 | 1848 | 3 | NA | 1 |
| 86885 | 245 | 2200 | 2 | NA | 1 |
| 86918 | 246 | 0 | 3 | 3 | 0 |
| 86918 | 246 | 349 | 3 | NA | 1 |
| 86918 | 246 | 1549 | 0 | NA | 1 |
| 87453 | 247 | 0 | 6 | 7 | 0 |
| 87453 | 247 | 606 | 4 | NA | 0 |
| 87453 | 247 | 1339 | 4 | NA | 0 |
| 87453 | 247 | 1423 | 3 | NA | 0 |
| 87453 | 247 | 1471 | 3 | NA | 0 |
| 87479 | 248 | 0 | 4 | 0 | 0 |
| 87479 | 248 | 351 | 4 | NA | 0 |
| 87517 | 249 | 0 | 3 | 0 | 0 |
| 87517 | 249 | 632 | 3 | NA | 0 |
| 88415 | 250 | 0 | 3 | 3 | 0 |
| 88415 | 250 | 711 | 1 | NA | 0 |
| 88415 | 250 | 1082 | 0 | NA | 0 |
| 88507 | 251 | 0 | 6 | 7 | 0 |
| 88507 | 251 | 962 | 3 | NA | 0 |
| 88507 | 251 | 1334 | 3 | NA | 0 |
| 88652 | 252 | 0 | 9 | 8 | 0 |
| 88652 | 252 | 781 | 3 | NA | 0 |
| 88652 | 252 | 1116 | 1 | NA | 0 |
| 88652 | 252 | 1952 | 1 | NA | 1 |
| 89282 | 253 | 0 | 6 | 8 | 0 |
| 89282 | 253 | 94 | 5 | 7 | 0 |
| 89282 | 253 | 363 | 4 | 6 | 0 |
| 89282 | 253 | 713 | 5 | 5 | 0 |
| 89282 | 253 | 1189 | 4 | NA | 1 |
| 89282 | 253 | 1469 | 3 | NA | 1 |
| 89282 | 253 | 1819 | 3 | NA | 1 |
| 89282 | 253 | 1935 | 3 | NA | 1 |
| 89282 | 253 | 2197 | 4 | NA | 1 |
| 90599 | 254 | 0 | 9 | 9 | 0 |
| 90599 | 254 | 351 | 7 | 9 | 0 |
| 90599 | 254 | 352 | 7 | 7 | 0 |
| 90599 | 254 | 382 | 7 | 7 | 0 |
| 90599 | 254 | 701 | 2 | 2 | 0 |
| 90599 | 254 | 1105 | 2 | NA | 1 |
| 90599 | 254 | 1434 | 1 | NA | 1 |
| 90697 | 255 | 0 | 8 | 8 | 0 |
| 90697 | 255 | 121 | 7 | 8 | 0 |
| 90697 | 255 | 406 | 7 | NA | 1 |
| 91036 | 256 | 0 | 5 | NA | 0 |
| 91036 | 256 | 371 | 4 | NA | 0 |
| 91036 | 256 | 1939 | 2 | NA | 1 |
| 91426 | 257 | 0 | 6 | NA | 1 |
| 91426 | 257 | 379 | 4 | NA | 1 |
| 92210 | 258 | 0 | 7 | NA | 0 |
| 92210 | 258 | 425 | 3 | NA | 0 |
| 92210 | 258 | 837 | 3 | NA | 0 |
| 92299 | 259 | 0 | 4 | 4 | 0 |
| 92299 | 259 | 379 | 3 | 3 | 0 |
| 92299 | 259 | 801 | 2 | 2 | 0 |
| 92299 | 259 | 1112 | 2 | 3 | 0 |
| 92299 | 259 | 1463 | 0 | 1 | 0 |
| 93678 | 260 | 0 | 8 | 9 | 0 |
| 93678 | 260 | 918 | 3 | NA | 0 |
| 93706 | 261 | 0 | 5 | 5 | 0 |
| 93706 | 261 | 664 | 4 | NA | 0 |
| 93706 | 261 | 708 | 4 | NA | 0 |
| 95733 | 262 | 0 | 6 | NA | 0 |
| 95733 | 262 | 362 | 6 | NA | 0 |
| 95733 | 262 | 1191 | 4 | NA | 1 |
| 96261 | 263 | 0 | 5 | 9 | 0 |
| 96261 | 263 | 308 | 4 | 4 | 0 |
| 96261 | 263 | 1418 | 2 | NA | 0 |
| 96353 | 264 | 0 | 4 | NA | 0 |
| 96353 | 264 | 482 | 4 | NA | 0 |
| 96353 | 264 | 816 | 3 | NA | 0 |
| 96382 | 265 | 0 | 5 | 5 | 0 |
| 96382 | 265 | 351 | 3 | 3 | 0 |
| 96382 | 265 | 756 | 3 | NA | 1 |
| 96382 | 265 | 1085 | 2 | NA | 1 |
| 96382 | 265 | 1435 | 0 | NA | 1 |
| 96382 | 265 | 1592 | 0 | NA | 1 |
| 96382 | 265 | 1818 | 1 | NA | 1 |
| 96419 | 266 | 0 | 2 | NA | 1 |
| 96419 | 266 | 349 | 1 | NA | 1 |
| 96B79 | 267 | 0 | 4 | 4 | 0 |
| 96B79 | 267 | 1242 | 2 | NA | 0 |
| 98152 | 268 | 0 | 7 | 7 | 0 |
| 98152 | 268 | 742 | 4 | 4 | 0 |
| 98195 | 269 | 0 | 6 | NA | 1 |
| 98195 | 269 | 349 | 2 | NA | 1 |
| 98521 | 270 | 0 | 3 | NA | 1 |
| 98521 | 270 | 345 | 2 | NA | 1 |
| 99121 | 271 | 0 | 5 | NA | 0 |
| 99121 | 271 | 370 | 1 | NA | 0 |
| 99121 | 271 | 869 | 2 | NA | 0 |
| 99842 | 272 | 0 | 8 | 10 | 0 |
| 99842 | 272 | 537 | 6 | NA | 0 |
| A2B2C | 273 | 0 | 4 | 5 | 0 |
| A2B2C | 273 | 159 | 4 | 4 | 0 |
| A2B2C | 273 | 368 | 2 | 3 | 0 |
| A2B2C | 273 | 3081 | 0 | NA | 1 |
| A3039 | 274 | 0 | 4 | 7 | 0 |
| A3039 | 274 | 1090 | 2 | NA | 0 |
| A543D | 275 | 0 | 8 | 9 | 0 |
| A543D | 275 | 444 | 4 | NA | 0 |
| A543D | 275 | 752 | 4 | NA | 0 |
| A6234 | 276 | 0 | 6 | 8 | 0 |
| A6234 | 276 | 356 | 4 | 5 | 0 |
| A6234 | 276 | 1113 | 2 | 4 | 0 |
| A6234 | 276 | 1242 | 2 | 4 | 0 |
| A6234 | 276 | 1551 | 2 | 5 | 0 |
| A6234 | 276 | 1847 | 3 | 4 | 0 |
| A6234 | 276 | 2221 | 3 | NA | 1 |
| A712D | 277 | 0 | 6 | 5 | 0 |
| A712D | 277 | 312 | 3 | 3 | 0 |
| B0712 | 278 | 0 | 7 | 8 | 0 |
| B0712 | 278 | 1122 | 2 | NA | 0 |
| B0712 | 278 | 1563 | 3 | NA | 0 |
| B0712 | 278 | 1858 | 3 | NA | 0 |
| B0F3B | 279 | 0 | 5 | 7 | 0 |
| B1311 | 280 | 0 | 4 | 2 | 0 |
| B1311 | 280 | 331 | 2 | 1 | 0 |
| B1311 | 280 | 1283 | 0 | 0 | 0 |
| B1311 | 280 | 1660 | 0 | 0 | 0 |
| B6069 | 281 | 0 | 9 | 9 | 0 |
| B6069 | 281 | 661 | 2 | 3 | 0 |
| C0102 | 282 | 0 | 4 | 6 | 0 |
| C0102 | 282 | 323 | 5 | 5 | 0 |
| C0102 | 282 | 676 | 4 | 6 | 0 |
| C0102 | 282 | 1463 | 4 | NA | 0 |
| C0D60 | 283 | 0 | 5 | 1 | 0 |
| C0D60 | 283 | 5 | 5 | 5 | 0 |
| C0D60 | 283 | 737 | 3 | 3 | 0 |
| C0D60 | 283 | 866 | 2 | 3 | 0 |
| C0D60 | 283 | 1087 | 2 | 2 | 0 |
| C0D60 | 283 | 1478 | 2 | 2 | 0 |
| C3530 | 284 | 0 | 5 | 6 | 0 |
| C3530 | 284 | 671 | 4 | 4 | 0 |
| C667D | 285 | 0 | 7 | 7 | 0 |
| C667D | 285 | 163 | 6 | 7 | 0 |
| C667D | 285 | 220 | 7 | 7 | 0 |
| C667D | 285 | 369 | 6 | 7 | 0 |
| C667D | 285 | 757 | 7 | 8 | 0 |
| C667D | 285 | 1118 | 7 | NA | 0 |
| D2B53 | 286 | 0 | 10 | 6 | 0 |
| D2B53 | 286 | 987 | 1 | 4 | 0 |
| D353A | 287 | 0 | 9 | 9 | 0 |
| D353A | 287 | 347 | 6 | 6 | 0 |
| D353A | 287 | 942 | 2 | 4 | 0 |
| D353A | 287 | 1309 | 2 | 0 | 0 |
| D353A | 287 | 1663 | 1 | 2 | 0 |
| D6642 | 288 | 0 | 4 | 3 | 0 |
| D6642 | 288 | 343 | 3 | 2 | 0 |
| D6642 | 288 | 386 | 3 | 2 | 0 |
| D6642 | 288 | 747 | 0 | 0 | 0 |
| D7A14 | 289 | 0 | 3 | 0 | 0 |
| D7A14 | 289 | 1461 | 2 | NA | 1 |
| E022A | 290 | 0 | 4 | NA | 0 |
| E022A | 290 | 356 | 4 | NA | 0 |
| E022A | 290 | 722 | 3 | NA | 0 |
| E0472 | 291 | 0 | 7 | 8 | 0 |
| E0472 | 291 | 951 | 3 | 5 | 0 |
| E0472 | 291 | 1330 | 3 | 4 | 0 |
| E0472 | 291 | 1722 | 1 | 3 | 0 |
| E0472 | 291 | 2065 | 2 | NA | 0 |
| E0472 | 291 | 2414 | 3 | NA | 0 |
| E1154 | 292 | 0 | 6 | 7 | 0 |
| E1154 | 292 | 252 | 5 | 6 | 0 |
| E1154 | 292 | 629 | 4 | 5 | 0 |
| E1B15 | 293 | 0 | 2 | 4 | 0 |
| E1B15 | 293 | 737 | 1 | 2 | 0 |
| E1B15 | 293 | 1478 | 0 | NA | 0 |
| E1B15 | 293 | 1851 | 0 | NA | 0 |
| E1D7D | 294 | 0 | 9 | 10 | 0 |
| E1D7D | 294 | 361 | 5 | 6 | 0 |
| E1D7D | 294 | 1014 | 2 | 4 | 0 |
| E2204 | 295 | 0 | 5 | 7 | 0 |
| E2204 | 295 | 321 | 3 | NA | 0 |
| E2204 | 295 | 501 | 4 | NA | 0 |
| E2204 | 295 | 859 | 4 | NA | 0 |
| E6D47 | 296 | 0 | 5 | NA | 1 |
| E7E64 | 297 | 0 | 8 | 8 | 0 |
| E7E64 | 297 | 985 | 4 | 5 | 0 |
| E7E64 | 297 | 1485 | 3 | 5 | 0 |
| F1071 | 298 | 0 | 6 | 4 | 0 |
| F1071 | 298 | 147 | 5 | 5 | 0 |
| F1071 | 298 | 304 | 1 | 3 | 0 |
| F1071 | 298 | 534 | 2 | 3 | 0 |
| F1071 | 298 | 711 | 2 | 3 | 0 |
| F1555 | 299 | 0 | 7 | 7 | 0 |
| F1555 | 299 | 321 | 0 | 4 | 0 |
| F5F25 | 300 | 0 | 7 | 7 | 0 |
| F5F25 | 300 | 257 | 6 | 8 | 0 |
| F5F25 | 300 | 422 | 6 | 7 | 0 |
| F5F25 | 300 | 626 | 5 | 7 | 0 |
| F5F25 | 300 | 1407 | 3 | NA | 0 |
| 03596 | 301 | 0 | 0 | 1 | 0 |
| 03596 | 301 | 347 | 0 | 1 | 0 |
| 03596 | 301 | 694 | 0 | 0 | 0 |
| 03596 | 301 | 1086 | 0 | 0 | 0 |
| 03596 | 301 | 2187 | 0 | NA | 1 |
| 13726 | 302 | 0 | 0 | 2 | 0 |
| 13726 | 302 | 786 | 0 | 2 | 0 |
| 13726 | 302 | 1486 | 0 | 1 | 0 |
| 13726 | 302 | 1933 | 0 | NA | 1 |
| 2184D | 303 | 0 | 2 | 5 | 0 |
| 2184D | 303 | 1307 | 0 | 3 | 0 |
| 41B20 | 304 | 0 | 1 | 2 | 0 |
| 41B20 | 304 | 343 | 0 | 1 | 0 |
| 41B20 | 304 | 683 | 0 | 3 | 0 |
| 41B20 | 304 | 1028 | 0 | 0 | 0 |
| 41B20 | 304 | 1813 | 0 | NA | 0 |
| B1757 | 305 | 0 | 4 | 4 | 0 |
| B1757 | 305 | 724 | 0 | 2 | 0 |
| B1757 | 305 | 1101 | 0 | NA | 0 |
| B1757 | 305 | 2659 | 0 | NA | 1 |
| B1757 | 305 | 2925 | 0 | NA | 1 |
| C6F06 | 306 | 0 | 3 | 4 | 0 |
| C6F06 | 306 | 482 | 0 | 1 | 0 |
| C6F06 | 306 | 1112 | 0 | NA | 0 |
| C6F06 | 306 | 1825 | 0 | 0 | 0 |
| D0701 | 307 | 0 | 0 | 1 | 0 |
| D0701 | 307 | 917 | 3 | 3 | 0 |
| D0701 | 307 | 1988 | 0 | 4 | 0 |
| D0701 | 307 | 2117 | 0 | 3 | 0 |
| D0701 | 307 | 2717 | 0 | 1 | 0 |
| D0701 | 307 | 3228 | 0 | NA | 1 |
1.1 Observed data
N individuals = 307
N samples = 1025
1.1.1 Pomona
ggplot() +
geom_line(data = observed.data.for.fitting.incl.neg,aes(x = time,y = titer.pomona,col = id,group = id),alpha = 0.4,size = 1,color="#9E0142") +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_classic(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 24),
text = element_text(size = 11),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_text(size=30,margin = ggplot2::margin(r = 10)),
legend.position = "none")+
xlab("Time since first positive") +
ylab("Ab level (log2)")
ggplot() +
geom_line(data = observed.data.for.fitting.incl.neg[1:400,],aes(x = time,y = titer.pomona,col = id,group = id),alpha = 0.4,size = 1,color="#9E0142") +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_classic(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 24),
text = element_text(size = 11),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_text(size=30,margin = ggplot2::margin(r = 10)),
legend.position = "none")+
xlab("Time since first positive") +
ylab("Ab level (log2)")
ggplot() +
geom_line(data = observed.data.for.fitting,aes(x = time,y = titer.pomona,col = id,group = id),alpha = 0.4,size = 0.6) +
geom_point(data = observed.data.for.fitting,aes(x = time,y = titer.pomona,col = id,group = id),size=1.2,alpha = 0.9) +
scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
# scale_y_continuous(limits = c(2,10),breaks = 2:10) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 24),
text = element_text(size = 11),
axis.text = element_text(size = 10),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = "none")+
xlab("Time since first positive sample") +
ylab("Antibody level (log2) Pomona")
1.1.2 Autumnalis
ggplot() +
geom_line(data = observed.data.for.fitting.incl.neg,aes(x = time,y = titer.aut,col = id,group = id),alpha = 0.4,size = 1,color="#9E0142") +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_classic(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 24),
text = element_text(size = 11),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_text(size=30,margin = ggplot2::margin(r = 10)),
legend.position = "none")+
xlab("Time since first positive") +
ylab("Ab level (log2)")
ggplot() +
geom_line(data = observed.data.for.fitting,aes(x = time,y = titer.aut,col = id,group = id),alpha = 0.4,size = 0.6) +
geom_point(data = observed.data.for.fitting,aes(x = time,y = titer.aut,col = id,group = id),size=1.2,alpha = 0.9) +
scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 24),
text = element_text(size = 11),
axis.text = element_text(size = 10),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = "none")+
xlab("Time since first positive sample") +
ylab("Antibody level (log2) Autumnalis")
1.1.3 Combined
plot.temp.dat = gather(observed.data.for.fitting,serovar,titer,c("titer.pomona","titer.aut"))
plot.temp.dat$id = paste0(plot.temp.dat$serovar,plot.temp.dat$id)
ggplot() +
geom_line(data = plot.temp.dat,aes(x = time,y = titer,col = serovar,group = id),alpha = 0.3,size = 0.7) +
#geom_point(data = observed.data.for.fitting.incl.neg,aes(x = time,y = titer.pomona,col = id,group = id),size=1.2,alpha = 0.9,col="grey") +
#geom_point(data = observed.data.for.fitting.incl.neg,aes(x = time,y = titer.aut,col = id,group = id),size=1.2,alpha = 0.9,col="darkred") +
#scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_color_manual(values = brewer.pal(11,"Spectral")[c(2,10)],labels = c("Autumnalis","Pomona"),name="Serovar") +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 24),
text = element_text(size = 11),
axis.text = element_text(size = 10),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = c(0.75,0.75),
legend.background = element_rect(colour = "transparent", fill = "transparent"))+
xlab("Time since first positive sample") +
ylab("Antibody level (log)")
Autumnalis levels increased by 0.2 for plotting purposes.
plot.temp.dat$titer[which(plot.temp.dat$serovar=="titer.aut")] = plot.temp.dat$titer[which(plot.temp.dat$serovar=="titer.aut")] + 0.2
ggplot() +
#geom_line(data = plot.temp.dat,aes(x = time,y = titer,col = serovar,group = id),alpha = 0.3,size = 0.7) +
geom_point(data = plot.temp.dat,aes(x = time,y = titer,col = serovar,group = id),alpha = 0.4,size = 0.7) +
#geom_point(data = observed.data.for.fitting.incl.neg,aes(x = time,y = titer.aut,col = id,group = id),size=1.2,alpha = 0.9,col="darkred") +
#scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_color_manual(values = brewer.pal(5,"Spectral")[c(1,5)],labels = c("Autumnalis","Pomona"),name="Serovar") +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 24),
text = element_text(size = 11),
axis.text = element_text(size = 10),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = c(0.75,0.75),
legend.background = element_rect(colour = "transparent", fill = "transparent"))+
xlab("Time since first positive sample") +
ylab("Antibody level (log)")
2 Fit model parameters
2.1 Double exponential
# set prior parameters
peak.titer.mean.prior.mean.pomona = 7
peak.titer.mean.prior.sd.pomona = 2
peak.titer.sd.prior.pomona = 2.5 # for multivariate normal prior
peak.titer.mean.prior.mean.aut = 7.5
peak.titer.mean.prior.sd.aut = 2
peak.titer.sd.prior.aut = 3 # for multivariate normal prior
decay.rate.mean.prior.shape.pomona = 1
decay.rate.mean.prior.rate.pomona = 20
decay.rate.sd.prior.shape.pomona = 2 # 7
decay.rate.sd.prior.rate.pomona = 3 # 20000
decay.rate.mean.prior.shape.aut = 1
decay.rate.mean.prior.rate.aut = 20
decay.rate.sd.prior.shape.aut = 2 # 7
decay.rate.sd.prior.rate.aut = 3 # 20000
# parameters:
# 1 = error.sd, individual (log) normal error
# 2 : 1+N.inds = time of infection, individual, as time before first positive
# 2+N.inds : 2+N.inds+N.inds = peak titer individual
N.inds = length(unique(observed.data.for.fitting$id))
uid = unique(observed.data.for.fitting$id)
# burn.in = 40000
# iterations = 50000 # = after burn in
burn.in = 10000
iterations = 50000 # = after burn in
run.mcmc=F
if(run.mcmc==T){
model1.mod = function(){
# priors
for(j in 1:length(neg.int)){
# multivariate distribution for pom and aut peak titers
mu_pom_aut[j,1:2] ~ dmnorm(mu_pom_aut_mean,mu_pom_aut_precision)
# extract mean peak levels for pom and aut
mu_pomona[j] <- mu_pom_aut[j,1]
mu_aut[j] <- mu_pom_aut[j,2]
# decay rates pom and aut
decay_pomona[j] ~ dnorm(decay_overall_pomona,decay_tau_overall_pomona)
decay_aut[j] ~ dnorm(decay_overall_aut,decay_tau_overall_aut)
# time between peak level and first positive, shared between pom and aut
theta[j] ~ dunif(neg.int[j],0)
}
sigma_pomona ~ dunif(0,50)
tau_pomona <- 1/(sigma_pomona*sigma_pomona)
sigma_aut ~ dunif(0,50)
tau_aut <- 1/(sigma_aut*sigma_aut)
lab_effect ~ dnorm(0,0.01)
#hyper priors
# multivariate pom aut mean and sd
mu_pom_aut_mean ~ dmnorm(mu_means,tau_means)
mu_pom_aut_precision ~ dwish(omega,wishdf)
# extract individual means peak level
mu_overall_pomona <- mu_pom_aut_mean[1]
mu_overall_aut <- mu_pom_aut_mean[2]
# multivariate precision matrix
inverse_mu_pom_aut_precision <- inverse(mu_pom_aut_precision)
sigma_overall_pomona <- inverse_mu_pom_aut_precision[1,1]^(1/2)
sigma_overall_aut <- inverse_mu_pom_aut_precision[2,2]^(1/2)
# decay rates
decay_overall_pomona ~ dgamma(decay.rate.mean.prior.shape.pomona,decay.rate.mean.prior.rate.pomona)
decay_sigma_overall_pomona ~ dgamma(decay.rate.sd.prior.shape.pomona,decay.rate.sd.prior.rate.pomona)
decay_tau_overall_pomona <- 1/(decay_sigma_overall_pomona*decay_sigma_overall_pomona)
decay_overall_aut ~ dgamma(decay.rate.mean.prior.shape.aut,decay.rate.mean.prior.rate.aut)
decay_sigma_overall_aut ~ dgamma(decay.rate.sd.prior.shape.aut,decay.rate.sd.prior.rate.aut)
decay_tau_overall_aut <- 1/(decay_sigma_overall_aut*decay_sigma_overall_aut)
# likelihood
for(i in 1:length(time)){
# predicted level pomona
titer_pred_pomona[i] <- lab_effect*lab[i] + mu_pomona[id[i]]*exp(-decay_pomona[id[i]]*(time[i]-theta[id[i]]))
true_titer_pomona[i] ~ dnorm(titer_pred_pomona[i],tau_pomona)
# interval censoring
titer_pomona[i] ~ dinterval(true_titer_pomona[i], c(1,2,3,4,5,6,7,8,9,10,11,12,13))
# predicted level aut
titer_pred_aut[i] <- lab_effect*lab[i] + mu_aut[id[i]]*exp(-decay_aut[id[i]]*(time[i]-theta[id[i]]))
true_titer_aut[i] ~ dnorm(titer_pred_aut[i],tau_aut)
# interval censoring
titer_aut[i] ~ dinterval(true_titer_aut[i], c(1,2,3,4,5,6,7,8,9,10,11,12,13))
# store sum loglikelihood as parameter for WAIC calculation
LogLik[i] = log(dnorm(titer_pomona[i],true_titer_pomona[i],tau_pomona)) + log(dnorm(titer_aut[i],true_titer_aut[i],tau_aut))
}
}
model.file = "jags_model.txt"
write.model(model1.mod,model.file)
model.jags = jags.model(model.file,
data=list('titer_pomona'=observed.data.for.fitting$titer.pomona,
'titer_aut'=observed.data.for.fitting$titer.aut,
'time'=observed.data.for.fitting$time,
'id'=observed.data.for.fitting$id,
'neg.int'=neg.intervals,
'lab'=observed.data.for.fitting$lab,
'mu_means' = c(peak.titer.mean.prior.mean.pomona,peak.titer.mean.prior.mean.aut),
'tau_means' = diag(c((1/(peak.titer.mean.prior.sd.pomona^2)),(1/(peak.titer.mean.prior.sd.aut^2)))),
'omega' = diag(c((1/(peak.titer.sd.prior.pomona^2)),(1/(peak.titer.sd.prior.aut^2)))),
'wishdf' = 2,
'decay.rate.mean.prior.shape.pomona' = decay.rate.mean.prior.shape.pomona,
'decay.rate.mean.prior.rate.pomona' = decay.rate.mean.prior.rate.pomona,
"decay.rate.sd.prior.shape.pomona" = decay.rate.sd.prior.shape.pomona,
"decay.rate.sd.prior.rate.pomona" = decay.rate.sd.prior.rate.pomona,
'decay.rate.mean.prior.shape.aut' = decay.rate.mean.prior.shape.aut,
'decay.rate.mean.prior.rate.aut' = decay.rate.mean.prior.rate.aut,
"decay.rate.sd.prior.shape.aut" = decay.rate.sd.prior.shape.aut,
"decay.rate.sd.prior.rate.aut" = decay.rate.sd.prior.rate.aut
),
#inits=list('beta0'=-1),
n.chains=6,
n.adapt = 1000)
# burn in
update(model.jags, n.iter = burn.in, by = 1)
# draw samples
post = coda.samples(model.jags, c("mu_overall_pomona","sigma_overall_pomona","decay_overall_pomona","decay_sigma_overall_pomona","decay_pomona","mu_overall_aut","sigma_overall_aut","decay_overall_aut","decay_sigma_overall_aut","decay_aut","theta","mu_pomona","mu_aut","LogLik","mu_pom_aut_mean","mu_pom_aut_precision","lab_effect"), n.iter = iterations, thin = 1)
chains.burn.df = mcmclist.to.dataframe(post)
rm(post)
# save output, needs existing MCMC_runs folder in working directory
filename = "MCMC_antibody_decay_double_exponential.RDS"
print(filename)
saveRDS(chains.burn.df,filename)
} else {
# load previous output if not re-running model, speeds up markdown knitting
chains.burn.df=readRDS("MCMC_antibody_decay_double_exponential.RDS")
}
chains.burn.df.original = chains.burn.df
# only keep last 20000 iterations for each chain
chains.burn.df = chains.burn.df %>%
filter(iteration > (max(iteration)-20000))
parnames = c(paste0("LogLik",1:nrow(observed.data.for.fitting)),paste0("decay.rate.aut.",uid),"decay.rate.overall.aut","decay.rate.overall.pomona",paste0("decay.rate.pomona.",uid),"decay.rate.sd.overall.aut","decay.rate.sd.overall.pomona","lab.effect",paste0("peak.titer.aut.",uid),"peak.titer.overall.aut","peak.titer.overall.pomona",paste0("mu_pom_aut_mean_pom_",1:2),paste0("mu_pom_aut_precision_",1:4),paste0("peak.titer.pomona.",uid),"peak.titer.sd.overall.aut","peak.titer.sd.overall.pomona",paste0("toi.",uid),"chain","iteration")
colnames(chains.burn.df) = parnames# gelman rubin diagnostics, uncomment to run
# gel.rub.apply.fun = function(x) gelman.fun(x = x,chains=chains.burn.df$chain,iterations = chains.burn.df$iteration)
# R.vals = gel.rub.apply.fun(chains.burn.df[,1])
# #
# gel.rub = data.frame(variable = colnames(chains.burn.df)[-which(colnames(chains.burn.df) %in% c("chain","iteration"))],
# R = apply(chains.burn.df[-which(colnames(chains.burn.df) %in% c("chain","iteration"))],2,gel.rub.apply.fun))
# # get posterior estimates of multivariate mean and precision matrix
mnorm_means = c(mean(chains.burn.df$mu_pom_aut_mean_pom_1),mean(chains.burn.df$mu_pom_aut_mean_pom_2))
precision_matrix = matrix(data = c(
median(chains.burn.df$mu_pom_aut_precision_1),
median(chains.burn.df$mu_pom_aut_precision_2),
median(chains.burn.df$mu_pom_aut_precision_3),
median(chains.burn.df$mu_pom_aut_precision_4)),ncol=2)2.2 Adjust time since first positive sample to estimated time since peak level
observed.data.for.fitting$time.since.peak = NA
for(i in 1:length(uid)){
idx.current = which(observed.data.for.fitting$id == uid[i])
peak.titer.time = round(max.dens.fun(x = chains.burn.df[,paste0("toi.",i)],neg.interval = neg.intervals[i]))
observed.data.for.fitting$time.since.peak[idx.current] = observed.data.for.fitting$time[idx.current] - peak.titer.time
}2.3 Single exponential
if(run.mcmc==T){
# set prior parameters
peak.titer.mean.prior.mean.pomona = 7.5
peak.titer.mean.prior.sd.pomona = 0.7
peak.titer.sd.prior.pomona = 3 # for multivariate normal prior
peak.titer.mean.prior.mean.aut = 7
peak.titer.mean.prior.sd.aut = 0.7
peak.titer.sd.prior.aut = 3 # for multivariate normal prior
decay.rate.mean.prior.mean.pomona = 0.0009
decay.rate.mean.prior.sd.pomona = 0.0001
decay.rate.sd.prior.shape.pomona = 7
decay.rate.sd.prior.rate.pomona = 20000
decay.rate.mean.prior.mean.aut = 0.0009
decay.rate.mean.prior.sd.aut = 0.0001
decay.rate.sd.prior.shape.aut = 7
decay.rate.sd.prior.rate.aut = 20000
# parameters:
# 1 = error.sd, individual (log) normal error
# 2 : 1+N.inds = time of infection, individual, as time before first positive
# 2+N.inds : 2+N.inds+N.inds = peak titer individual
N.inds = length(unique(observed.data.for.fitting$id))
uid = unique(observed.data.for.fitting$id)
burn.in = 40000
iterations = 50000 # = after burn in
model1.mod = function(){
# priors
for(j in 1:length(neg.int)){
# multivariate distribution for pom and aut peak titers
mu_pom_aut[j,1:2] ~ dmnorm(mu_pom_aut_mean,mu_pom_aut_precision)
# extract mean peak levels for pom and aut
mu_pomona[j] <- mu_pom_aut[j,1]
mu_aut[j] <- mu_pom_aut[j,2]
# decay rates pom and aut
decay_pomona[j] ~ dnorm(decay_overall_pomona,decay_tau_overall_pomona)
decay_aut[j] ~ dnorm(decay_overall_aut,decay_tau_overall_aut)
# time between peak level and first positive, shared between pom and aut
theta[j] ~ dunif(neg.int[j],0)
}
sigma_pomona ~ dunif(0,50)
tau_pomona <- 1/(sigma_pomona*sigma_pomona)
sigma_aut ~ dunif(0,50)
tau_aut <- 1/(sigma_aut*sigma_aut)
lab_effect ~ dnorm(0,0.01)
#hyper priors
# multivariate pom aut mean and sd
mu_pom_aut_mean ~ dmnorm(mu_means,tau_means)
mu_pom_aut_precision ~ dwish(omega,wishdf)
# extract individual means peak level
mu_overall_pomona <- mu_pom_aut_mean[1]
mu_overall_aut <- mu_pom_aut_mean[2]
# multivariate precision matrix
inverse_mu_pom_aut_precision <- inverse(mu_pom_aut_precision)
sigma_overall_pomona <- inverse_mu_pom_aut_precision[1,1]^(1/2)
sigma_overall_aut <- inverse_mu_pom_aut_precision[2,2]^(1/2)
# decay rates
decay_overall_pomona ~ dnorm(decay.rate.mean.prior.mean.pomona,1/(decay.rate.mean.prior.sd.pomona*decay.rate.mean.prior.sd.pomona))
decay_sigma_overall_pomona ~ dgamma(decay.rate.sd.prior.shape.pomona,decay.rate.sd.prior.rate.pomona)
decay_tau_overall_pomona <- 1/(decay_sigma_overall_pomona*decay_sigma_overall_pomona)
decay_overall_aut ~ dnorm(decay.rate.mean.prior.mean.aut,1/(decay.rate.mean.prior.sd.aut*decay.rate.mean.prior.sd.aut))
decay_sigma_overall_aut ~ dgamma(decay.rate.sd.prior.shape.aut,decay.rate.sd.prior.rate.aut)
decay_tau_overall_aut <- 1/(decay_sigma_overall_aut*decay_sigma_overall_aut)
# likelihood
for(i in 1:length(time)){
# predicted level pomona
titer_pred_pomona[i] <- lab_effect*lab[i] +mu_pomona[id[i]] - decay_pomona[id[i]]*(time[i]-theta[id[i]])
true_titer_pomona[i] ~ dnorm(titer_pred_pomona[i],tau_pomona)
# interval censoring
titer_pomona[i] ~ dinterval(true_titer_pomona[i], c(1,2,3,4,5,6,7,8,9,10,11,12,13))
# predicted level aut
titer_pred_aut[i] <- lab_effect*lab[i] +mu_aut[id[i]] - decay_aut[id[i]]*(time[i]-theta[id[i]])
true_titer_aut[i] ~ dnorm(titer_pred_aut[i],tau_aut)
# interval censoring
titer_aut[i] ~ dinterval(true_titer_aut[i], c(1,2,3,4,5,6,7,8,9,10,11,12,13))
# store sum loglikelihood as parameter for WAIC calculation
LogLik[i] = log(dnorm(titer_pomona[i],true_titer_pomona[i],tau_pomona)) + log(dnorm(titer_aut[i],true_titer_aut[i],tau_aut))
}
}
model.file = "jags_model.txt"
write.model(model1.mod,model.file)
model.jags = jags.model(model.file,
data=list('titer_pomona'=observed.data.for.fitting$titer.pomona,
'titer_aut'=observed.data.for.fitting$titer.aut,
'time'=observed.data.for.fitting$time,
'id'=observed.data.for.fitting$id,
'lab'=observed.data.for.fitting$lab,
'neg.int'=neg.intervals,
'mu_means' = c(peak.titer.mean.prior.mean.pomona,peak.titer.mean.prior.mean.aut),
'tau_means' = diag(c((1/(peak.titer.mean.prior.sd.pomona^2)),(1/(peak.titer.mean.prior.sd.aut^2)))),
'omega' = diag(c((1/(peak.titer.sd.prior.pomona^2)),(1/(peak.titer.sd.prior.aut^2)))),
'wishdf' = 2,
"decay.rate.mean.prior.mean.pomona" = decay.rate.mean.prior.mean.pomona,
"decay.rate.mean.prior.sd.pomona" = decay.rate.mean.prior.sd.pomona,
"decay.rate.sd.prior.shape.pomona" = decay.rate.sd.prior.shape.pomona,
"decay.rate.sd.prior.rate.pomona" = decay.rate.sd.prior.rate.pomona,
"decay.rate.mean.prior.mean.aut" = decay.rate.mean.prior.mean.aut,
"decay.rate.mean.prior.sd.aut" = decay.rate.mean.prior.sd.aut,
"decay.rate.sd.prior.shape.aut" = decay.rate.sd.prior.shape.aut,
"decay.rate.sd.prior.rate.aut" = decay.rate.sd.prior.rate.aut
),
#inits=list('beta0'=-1),
n.chains=6,
n.adapt = 2500)
# burn in
update(model.jags, n.iter = burn.in, by = 1)
# draw samples
post = coda.samples(model.jags, c("mu_overall_pomona","sigma_overall_pomona","decay_overall_pomona","decay_sigma_overall_pomona","decay_pomona","mu_overall_aut","sigma_overall_aut","decay_overall_aut","decay_sigma_overall_aut","decay_aut","theta","mu_pomona","mu_aut","LogLik","mu_pom_aut_mean","lab_effect"), n.iter = iterations, thin = 10)
chains.burn.df = mcmclist.to.dataframe(post)
# parnames = c(paste0("LogLik",1:nrow(observed.data.for.fitting)),paste0("decay.rate.aut.",uid),"decay.rate.overall.aut","decay.rate.overall.pomona",paste0("decay.rate.pomona.",uid),"decay.rate.sd.overall.aut","decay.rate.sd.overall.pomona",paste0("peak.titer.aut.",uid),"peak.titer.overall.aut","peak.titer.overall.pomona",paste0("mu_pom_aut_mean_pom_",1:2),paste0("mu_pom_aut_precision_",1:4),paste0("peak.titer.pomona.",uid),"peak.titer.sd.overall.aut","peak.titer.sd.overall.pomona",paste0("toi.",uid),"chain","iteration")
#
# colnames(chains.burn.df) = parnames
# save output, needs existing MCMC_runs folder in working directory
filename = "MCMC_antibody_decay_single_exponential.RDS"
print(filename)
saveRDS(chains.burn.df,filename)
} else {
# load previous output if not re-running model, speeds up markdown knitting
chains.burn.df.1=readRDS("MCMC_antibody_decay_single_exponential.RDS")
}2.4 Power
if(run.mcmc==T){
# set prior parameters
peak.titer.mean.prior.mean.pomona = 7.5
peak.titer.mean.prior.sd.pomona = 0.7
peak.titer.sd.prior.pomona = 3 # for multivariate normal prior
peak.titer.mean.prior.mean.aut = 7
peak.titer.mean.prior.sd.aut = 0.7
peak.titer.sd.prior.aut = 3 # for multivariate normal prior
shape.sd.prior = 3
scale.sd.prior = 0.003
shape.mean.sd.prior = 1
scale.mean.sd.prior = 0.0005
log.mean.shape.prior.mean = log(0.5)
log.mean.shape.prior.sd = 0.1
log.mean.scale.prior.mean = log(0.0005)
log.mean.scale.prior.sd = 1
log.sd.shape.prior.shape = 2
log.sd.shape.prior.rate = 0.75
log.sd.scale.prior.shape = 2
log.sd.scale.prior.rate = 0.5
# parameters:
# 1 = error.sd, individual (log) normal error
# 2 : 1+N.inds = time of infection, individual, as time before first positive
# 2+N.inds : 2+N.inds+N.inds = peak titer individual
N.inds = length(unique(observed.data.for.fitting$id))
uid = unique(observed.data.for.fitting$id)
burn.in = 40000
iterations = 50000 # = after burn in
model1.mod = function(){
# priors
for(j in 1:length(neg.int)){
# multivariate distribution for pom and aut peak titers
mu_pom_aut[j,1:2] ~ dmnorm(mu_pom_aut_mean,mu_pom_aut_precision)
# extract mean peak levels for pom and aut
mu_pomona[j] <- mu_pom_aut[j,1]
exp.mu_pomona[j] <- 100*2^(mu_pomona[j]-1)
mu_aut[j] <- mu_pom_aut[j,2]
exp.mu_aut[j] <- 100*2^(mu_aut[j]-1)
# decay parameters pom and aut
log_shape_scale_pomona[j,1:2] ~ dmnorm(shape_scale_pomona_mean,shape_scale_pomona_precision)
log.shape_pomona[j] <- log_shape_scale_pomona[j,1]
log.scale_pomona[j] <- log_shape_scale_pomona[j,2]
shape_pomona[j] <- exp(log.shape_pomona[j]) + 1
scale_pomona[j] <- exp(log.scale_pomona[j])
log_shape_scale_aut[j,1:2] ~ dmnorm(shape_scale_aut_mean,shape_scale_aut_precision)
log.shape_aut[j] <- log_shape_scale_aut[j,1]
log.scale_aut[j] <- log_shape_scale_aut[j,2]
shape_aut[j] <- exp(log.shape_aut[j]) + 1
scale_aut[j] <- exp(log.scale_aut[j])
# time between peak level and first positive, shared between pom and aut
theta[j] ~ dunif(neg.int[j],0)
}
lab_effect ~ dnorm(0,0.01)
sigma_pomona ~ dunif(0,50)
tau_pomona <- 1/(sigma_pomona*sigma_pomona)
sigma_aut ~ dunif(0,50)
tau_aut <- 1/(sigma_aut*sigma_aut)
#hyper priors
# multivariate pom aut mean and sd
mu_pom_aut_mean ~ dmnorm(mu_means,tau_means)
mu_pom_aut_precision ~ dwish(omega,wishdf)
# extract individual means peak level
mu_overall_pomona <- mu_pom_aut_mean[1]
mu_overall_aut <- mu_pom_aut_mean[2]
# multivariate precision matrix
inverse_mu_pom_aut_precision <- inverse(mu_pom_aut_precision)
sigma_overall_pomona <- inverse_mu_pom_aut_precision[1,1]^(1/2)
sigma_overall_aut <- inverse_mu_pom_aut_precision[2,2]^(1/2)
# multivariate decay rate parameters pomona
shape_scale_pomona_mean ~ dmnorm(mean_shape_scale_pomona_mean,mean_shape_scale_pomona_tau)
shape_scale_pomona_precision ~ dwish(omega_shape_scale_pomona,wishdf_shape_scale_pomona)
# extract individual means decay rate parameters pomona
mu_overall_shape_pomona <- exp(shape_scale_pomona_mean[1])+1
mu_overall_scale_pomona <- exp(shape_scale_pomona_mean[2])
# multivariate precision matrix pomona
inverse_mu_shape_scale_pomona_precision <- inverse(shape_scale_pomona_precision)
sigma_overall_shape_pomona <- inverse_mu_shape_scale_pomona_precision[1,1]^(1/2)
sigma_overall_scale_pomona <- inverse_mu_shape_scale_pomona_precision[2,2]^(1/2)
# multivariate decay rate parameters aut
shape_scale_aut_mean ~ dmnorm(mean_shape_scale_aut_mean,mean_shape_scale_aut_tau)
shape_scale_aut_precision ~ dwish(omega_shape_scale_aut,wishdf_shape_scale_aut)
# extract individual means decay rate parameters aut
mu_overall_shape_aut <- exp(shape_scale_aut_mean[1])+1
mu_overall_scale_aut <- exp(shape_scale_aut_mean[2])
# multivariate precision matrix aut
inverse_mu_shape_scale_aut_precision <- inverse(shape_scale_aut_precision)
sigma_overall_shape_aut <- inverse_mu_shape_scale_aut_precision[1,1]^(1/2)
sigma_overall_scale_aut <- inverse_mu_shape_scale_aut_precision[2,2]^(1/2)
# likelihood
for(i in 1:length(time)){
# predicted level pomona
titer_pred_pomona[i] <- exp.mu_pomona[id[i]]*(1+(shape_pomona[id[i]]-1)*(exp.mu_pomona[id[i]]^(shape_pomona[id[i]]-1))*scale_pomona[id[i]]*(time[i]-theta[id[i]]))^(-1/(shape_pomona[id[i]]-1))
log2_titer_pred_pomona[i] <- 1+(log(titer_pred_pomona[i]/100)/log(2))
lab_effect_log2_titer_pred_pomona[i] <- lab_effect*lab[i] + log2_titer_pred_pomona[i]
true_titer_pomona[i] ~ dnorm(lab_effect_log2_titer_pred_pomona[i],tau_pomona)
# interval censoring
titer_pomona[i] ~ dinterval(true_titer_pomona[i], c(1,2,3,4,5,6,7,8,9,10,11,12,13))
titer_pred_aut[i] <- exp.mu_aut[id[i]]*(1+(shape_aut[id[i]]-1)*(exp.mu_aut[id[i]]^(shape_aut[id[i]]-1))*scale_aut[id[i]]*(time[i]-theta[id[i]]))^(-1/(shape_aut[id[i]]-1))
log2_titer_pred_aut[i] <- 1+(log(titer_pred_aut[i]/100)/log(2))
lab_effect_log2_titer_pred_aut[i] <- lab_effect*lab[i] + log2_titer_pred_aut[i]
true_titer_aut[i] ~ dnorm(lab_effect_log2_titer_pred_aut[i],tau_aut)
# interval censoring
titer_aut[i] ~ dinterval(true_titer_aut[i], c(1,2,3,4,5,6,7,8,9,10,11,12,13))
# store sum loglikelihood as parameter for WAIC calculation
LogLik[i] = log(dnorm(titer_pomona[i],true_titer_pomona[i],tau_pomona)) + log(dnorm(titer_aut[i],true_titer_aut[i],tau_aut))
}
}
model2.uncorrelated_shape_scale.mod = function(){
# priors
for(j in 1:length(neg.int)){
# multivariate distribution for pom and aut peak titers
mu_pom_aut[j,1:2] ~ dmnorm(mu_pom_aut_mean,mu_pom_aut_precision)
# extract mean peak levels for pom and aut
mu_pomona[j] <- mu_pom_aut[j,1]
exp.mu_pomona[j] <- 100*2^(mu_pomona[j]-1)
mu_aut[j] <- mu_pom_aut[j,2]
exp.mu_aut[j] <- 100*2^(mu_aut[j]-1)
# decay parameters pom and aut
log.shape_pomona[j] ~ dnorm(log_shape_pomona_overall_mean,log_shape_pomona_overall_tau)
log.scale_pomona[j] ~ dnorm(log_scale_pomona_overall_mean,log_scale_pomona_overall_tau)
shape_pomona[j] <- exp(log.shape_pomona[j]) + 1
scale_pomona[j] <- exp(log.scale_pomona[j])
log.shape_aut[j] ~ dnorm(log_shape_aut_overall_mean,log_shape_aut_overall_tau)
log.scale_aut[j] ~ dnorm(log_scale_aut_overall_mean,log_scale_aut_overall_tau)
shape_aut[j] <- exp(log.shape_aut[j]) + 1
scale_aut[j] <- exp(log.scale_aut[j])
# time between peak level and first positive, shared between pom and aut
theta[j] ~ dunif(neg.int[j],0)
}
lab_effect ~ dnorm(0,0.01)
sigma_pomona ~ dunif(0,50)
tau_pomona <- 1/(sigma_pomona*sigma_pomona)
sigma_aut ~ dunif(0,50)
tau_aut <- 1/(sigma_aut*sigma_aut)
#hyper priors
# multivariate pom aut mean and sd
mu_pom_aut_mean ~ dmnorm(mu_means,tau_means)
mu_pom_aut_precision ~ dwish(omega,wishdf)
# extract serovar peak level means
mu_overall_pomona <- mu_pom_aut_mean[1]
mu_overall_aut <- mu_pom_aut_mean[2]
# multivariate precision matrix
inverse_mu_pom_aut_precision <- inverse(mu_pom_aut_precision)
sigma_overall_pomona <- inverse_mu_pom_aut_precision[1,1]^(1/2)
sigma_overall_aut <- inverse_mu_pom_aut_precision[2,2]^(1/2)
# decay rate parameters pomona
log_shape_pomona_overall_mean ~ dnorm(log_shape_pomona_overall_mean_prior_mean,1/(log_shape_pomona_overall_mean_prior_sd*log_shape_pomona_overall_mean_prior_sd))
log_shape_pomona_overall_sd ~ dgamma(log_shape_pomona_overall_sd_prior_shape,log_shape_pomona_overall_sd_prior_rate)
log_shape_pomona_overall_tau <- 1/(log_shape_pomona_overall_sd*log_shape_pomona_overall_sd)
shape_pomona_overall_mean <- exp(log_shape_pomona_overall_mean) + 1
log_scale_pomona_overall_mean ~ dnorm(log_scale_pomona_overall_mean_prior_mean,1/(log_scale_pomona_overall_mean_prior_sd*log_scale_pomona_overall_mean_prior_sd))
log_scale_pomona_overall_sd ~ dgamma(log_scale_pomona_overall_sd_prior_shape,log_scale_pomona_overall_sd_prior_rate)
log_scale_pomona_overall_tau <- 1/(log_scale_pomona_overall_sd*log_scale_pomona_overall_sd)
scale_pomona_overall_mean <- exp(log_scale_pomona_overall_mean)
# decay rate parameters aut
log_shape_aut_overall_mean ~ dnorm(log_shape_aut_overall_mean_prior_mean,1/(log_shape_aut_overall_mean_prior_sd*log_shape_aut_overall_mean_prior_sd))
log_shape_aut_overall_sd ~ dgamma(log_shape_aut_overall_sd_prior_shape,log_shape_aut_overall_sd_prior_rate)
log_shape_aut_overall_tau <- 1/(log_shape_aut_overall_sd*log_shape_aut_overall_sd)
shape_aut_overall_mean <- exp(log_shape_aut_overall_mean) + 1
log_scale_aut_overall_mean ~ dnorm(log_scale_aut_overall_mean_prior_mean,1/(log_scale_aut_overall_mean_prior_sd*log_scale_aut_overall_mean_prior_sd))
log_scale_aut_overall_sd ~ dgamma(log_scale_aut_overall_sd_prior_shape,log_scale_aut_overall_sd_prior_rate)
log_scale_aut_overall_tau <- 1/(log_scale_aut_overall_sd*log_scale_aut_overall_sd)
scale_aut_overall_mean <- exp(log_scale_aut_overall_mean)
# likelihood
for(i in 1:length(time)){
# predicted level pomona
titer_pred_pomona[i] <- exp.mu_pomona[id[i]]*(1+(shape_pomona[id[i]]-1)*(exp.mu_pomona[id[i]]^(shape_pomona[id[i]]-1))*scale_pomona[id[i]]*(time[i]-theta[id[i]]))^(-1/(shape_pomona[id[i]]-1))
log2_titer_pred_pomona[i] <- 1+(log(titer_pred_pomona[i]/100)/log(2))
lab_effect_log2_titer_pred_pomona[i] <- lab_effect*lab[i] + log2_titer_pred_pomona[i]
true_titer_pomona[i] ~ dnorm(lab_effect_log2_titer_pred_pomona[i],tau_pomona)
# interval censoring
titer_pomona[i] ~ dinterval(true_titer_pomona[i], c(1,2,3,4,5,6,7,8,9,10,11,12,13))
titer_pred_aut[i] <- exp.mu_aut[id[i]]*(1+(shape_aut[id[i]]-1)*(exp.mu_aut[id[i]]^(shape_aut[id[i]]-1))*scale_aut[id[i]]*(time[i]-theta[id[i]]))^(-1/(shape_aut[id[i]]-1))
log2_titer_pred_aut[i] <- 1+(log(titer_pred_aut[i]/100)/log(2))
lab_effect_log2_titer_pred_aut[i] <- lab_effect*lab[i] + log2_titer_pred_aut[i]
true_titer_aut[i] ~ dnorm(lab_effect_log2_titer_pred_aut[i],tau_aut)
# interval censoring
titer_aut[i] ~ dinterval(true_titer_aut[i], c(1,2,3,4,5,6,7,8,9,10,11,12,13))
# store sum loglikelihood as parameter for WAIC calculation
LogLik[i] = log(dnorm(titer_pomona[i],true_titer_pomona[i],tau_pomona)) + log(dnorm(titer_aut[i],true_titer_aut[i],tau_aut))
}
}
model.file = "jags_model.txt"
write.model(model2.uncorrelated_shape_scale.mod,model.file)
model.jags = jags.model(model.file,
data=list('titer_pomona'=observed.data.for.fitting$titer.pomona,
'titer_aut'=observed.data.for.fitting$titer.aut,
'time'=observed.data.for.fitting$time,
'id'=observed.data.for.fitting$id,
'neg.int'=neg.intervals,
'lab'=observed.data.for.fitting$lab,
'mu_means' = c(peak.titer.mean.prior.mean.pomona,peak.titer.mean.prior.mean.aut),
'tau_means' = diag(c((1/(peak.titer.mean.prior.sd.pomona^2)),(1/(peak.titer.mean.prior.sd.aut^2)))),
'omega' = diag(c((1/(peak.titer.sd.prior.pomona^2)),(1/(peak.titer.sd.prior.aut^2)))),
'wishdf' = 2,
'log_shape_pomona_overall_mean_prior_mean' = log.mean.shape.prior.mean,
'log_shape_pomona_overall_mean_prior_sd' = log.mean.shape.prior.sd,
'log_scale_pomona_overall_mean_prior_mean' = log.mean.scale.prior.mean,
'log_scale_pomona_overall_mean_prior_sd' = log.mean.scale.prior.sd,
'log_shape_aut_overall_mean_prior_mean' = log.mean.shape.prior.mean,
'log_shape_aut_overall_mean_prior_sd' = log.mean.shape.prior.sd,
'log_scale_aut_overall_mean_prior_mean' = log.mean.scale.prior.mean,
'log_scale_aut_overall_mean_prior_sd' = log.mean.scale.prior.sd,
'log_shape_pomona_overall_sd_prior_shape' = log.sd.shape.prior.shape,
'log_shape_pomona_overall_sd_prior_rate' = log.sd.shape.prior.rate,
'log_scale_pomona_overall_sd_prior_shape' = log.sd.scale.prior.shape,
'log_scale_pomona_overall_sd_prior_rate' = log.sd.scale.prior.rate,
'log_shape_aut_overall_sd_prior_shape' = log.sd.shape.prior.shape,
'log_shape_aut_overall_sd_prior_rate' = log.sd.shape.prior.rate,
'log_scale_aut_overall_sd_prior_shape' = log.sd.scale.prior.shape,
'log_scale_aut_overall_sd_prior_rate' = log.sd.scale.prior.rate
),
#inits=list('beta0'=-1),
n.chains=6,
n.adapt = 5000)
# burn in
update(model.jags, n.iter = burn.in, by = 1)
# draw samples
post = coda.samples(model.jags, c("mu_overall_pomona","sigma_overall_pomona","mu_overall_aut","sigma_overall_aut","theta","LogLik","mu_pomona","mu_aut","mu_pom_aut_mean","shape_pomona_overall_mean","scale_pomona_overall_mean","shape_aut_overall_mean","scale_aut_overall_mean","shape_pomona","shape_aut","scale_pomona","scale_aut","log_shape_pomona_overall_sd","log_scale_pomona_overall_sd","log_shape_aut_overall_sd","log_scale_aut_overall_sd","lab_effect"), n.iter = iterations, thin = 10)
chains.burn.df = mcmclist.to.dataframe(post)
# save output, needs existing MCMC_runs folder in working directory
filename = "MCMC_antibody_decay_power"
print(filename)
saveRDS(chains.burn.df,filename)
} else {
# load previous output if not re-running model, speeds up markdown knitting
chains.burn.df.3=readRDS("MCMC_antibody_decay_power.RDS")
}2.5 Gather output three functions
if(run.mcmc==T) {
chains.burn.df.2 = chains.burn.df.original
N.inds = length(grep("theta",names(chains.burn.df.1)))
# get posterior estimates
post.overall = data.frame(model = 1:3,
RMSE = NA,
WAIC = NA,
LOOIC = NA,
peak.titer.mean = NA,
peak.titer.aut.mean = NA,
peak.titer.mean.95lo = NA,
peak.titer.mean.95hi = NA,
peak.titer.sd = NA,
peak.titer.sd.95lo = NA,
peak.titer.sd.95hi = NA,
decay.mean = NA,
decay.aut.mean = NA,
decay.mean.95lo = NA,
decay.mean.95hi = NA,
shape.mean = NA,
shape.mean.95lo = NA,
shape.mean.95hi = NA,
scale.mean = NA,
scale.mean.95lo = NA,
scale.mean.95hi = NA
)
post.individual = data.frame(model = rep(1:3,each = N.inds),
id = rep(1:N.inds,3),
toi.mean = NA,
toi.mean.95lo = NA,
toi.mean.95hi = NA,
toi.95.information.gained = NA,
toi.mean.50lo = NA,
toi.mean.50hi = NA,
toi.50.information.gained = NA,
peak.titer.mean = NA,
peak.titer.mean.95lo = NA,
peak.titer.mean.95hi = NA,
decay.rate.mean = NA,
decay.rate.mean.95lo = NA,
decay.rate.mean.95hi = NA,
shape.mean = NA,
shape.mean.95lo = NA,
shape.mean.95hi = NA,
scale.mean = NA,
scale.mean.95lo = NA,
scale.mean.95hi = NA
)
models = 1:3
for(model in 1:length(models)){
chains.burn.df.current = get(paste0("chains.burn.df.",model))
# pop level parameters
chains.burn.df.loglik = chains.burn.df.current[,grep("LogLik",colnames(chains.burn.df.current))]
chains.burn.df.loglik = chains.burn.df.loglik[is.finite(rowSums(chains.burn.df.loglik)),]
loglik.all.matrix = as.matrix(chains.burn.df.loglik)
rm(chains.burn.df.loglik)
WAIC.val = waic(loglik.all.matrix)
post.overall[model,"WAIC"] = round(WAIC.val$estimates["waic",][1],1)
LOOIC.val = loo(loglik.all.matrix)
post.overall[model,"LOOIC"] = round(LOOIC.val$estimates["looic",][1],1)
dens = density(chains.burn.df.current$mu_overall_pomona)
hpd.int.peak.titer = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
peak.titer.overall.mean = mean(chains.burn.df.current$mu_overall_pomona)
post.overall[model,"peak.titer.mean"] = peak.titer.overall.mean
post.overall[model,"peak.titer.mean.95lo"] = hpd.int.peak.titer[1]
post.overall[model,"peak.titer.mean.95hi"] = hpd.int.peak.titer[2]
dens = density(chains.burn.df.current$mu_overall_aut)
peak.titer.overall.mean = mean(chains.burn.df.current$mu_overall_aut)
post.overall[model,"peak.titer.aut.mean"] = peak.titer.overall.mean
dens = density(chains.burn.df.current$sigma_overall_pomona)
hpd.int.peak.titer.sd = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
peak.titer.sd.overall.mean = mean(chains.burn.df.current$sigma_overall_pomona)
post.overall[model,"peak.titer.sd"] = peak.titer.sd.overall.mean
post.overall[model,"peak.titer.sd.95lo"] = hpd.int.peak.titer.sd[1]
post.overall[model,"peak.titer.sd.95hi"] = hpd.int.peak.titer.sd[2]
if(model %in% c(1,2)) {
dens = density(chains.burn.df.current$decay_overall_pomona)
hpd.int.decay = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
decay.overall.mean = mean(chains.burn.df.current$decay_overall_pomona)
post.overall[model,"decay.mean"] = decay.overall.mean
post.overall[model,"decay.mean.95lo"] = hpd.int.decay[1]
post.overall[model,"decay.mean.95hi"] = hpd.int.decay[2]
dens = density(chains.burn.df.current$decay_overall_aut)
hpd.int.decay = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
decay.overall.mean = mean(chains.burn.df.current$decay_overall_aut)
post.overall[model,"decay.aut.mean"] = decay.overall.mean
} else {
dens = density(chains.burn.df.current$shape_pomona_overall_mean)
hpd.int.shape = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
shape.overall.mean = mean(chains.burn.df.current$shape_pomona_overall_mean)
post.overall[model,"shape.mean"] = shape.overall.mean
post.overall[model,"shape.mean.95lo"] = hpd.int.shape[1]
post.overall[model,"shape.mean.95hi"] = hpd.int.shape[2]
dens = density(chains.burn.df.current$scale_pomona_overall_mean)
hpd.int.scale = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
scale.overall.mean = mean(chains.burn.df.current$scale_pomona_overall_mean)
post.overall[model,"scale.mean"] = scale.overall.mean
post.overall[model,"scale.mean.95lo"] = hpd.int.scale[1]
post.overall[model,"scale.mean.95hi"] = hpd.int.scale[2]
}
# individual level parameters
for(i in 1:N.inds){
post.individual[which(post.individual$id==i & post.individual$model==model),"toi.mean"] = max.dens.fun(chains.burn.df.current[,paste0("theta[",i,"]")])
dens = density(chains.burn.df.current[,paste0("theta[",i,"]")])
hpd.int.toi = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
post.individual[which(post.individual$id==i & post.individual$model==model),"toi.mean.95lo"] = hpd.int.toi[1]
post.individual[which(post.individual$id==i & post.individual$model==model),"toi.mean.95hi"] = hpd.int.toi[2]
post.individual[which(post.individual$id==i & post.individual$model==model),"toi.95.information.gained"] = round(1-(hpd.int.toi[2]-hpd.int.toi[1])/abs(neg.intervals[i]),3)
hpd.int.toi = HDInterval::hdi(dens,credMass = 0.5,allowSplit=F)
post.individual[which(post.individual$id==i & post.individual$model==model),"toi.mean.50lo"] = hpd.int.toi[1]
post.individual[which(post.individual$id==i & post.individual$model==model),"toi.mean.50hi"] = hpd.int.toi[2]
post.individual[which(post.individual$id==i & post.individual$model==model),"toi.50.information.gained"] = round(1-(hpd.int.toi[2]-hpd.int.toi[1])/abs(neg.intervals[i]),3)
post.individual[which(post.individual$id==i & post.individual$model==model),"peak.titer.mean"] = mean(chains.burn.df.current[,paste0("mu_pomona[",i,"]")])
dens = density(chains.burn.df.current[,paste0("mu_pomona[",i,"]")])
hpd.int.peak.titer = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
post.individual[which(post.individual$id==i & post.individual$model==model),"peak.titer.mean.95lo"] = hpd.int.peak.titer[1]
post.individual[which(post.individual$id==i & post.individual$model==model),"peak.titer.mean.95hi"] = hpd.int.peak.titer[2]
if(model %in% 1:2) {
post.individual[which(post.individual$id==i & post.individual$model==model),"decay.rate.mean"] = mean(chains.burn.df.current[,paste0("decay_pomona[",i,"]")])
dens = density(chains.burn.df.current[,paste0("decay_pomona[",i,"]")])
hpd.int.peak.titer = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
post.individual[which(post.individual$id==i & post.individual$model==model),"decay.rate.mean.95lo"] = hpd.int.peak.titer[1]
post.individual[which(post.individual$id==i & post.individual$model==model),"decay.rate.mean.95hi"] = hpd.int.peak.titer[2]
}
if(model == 3) {
post.individual[which(post.individual$id==i & post.individual$model==model),"shape.mean"] = mean(chains.burn.df.current[,paste0("shape_pomona[",i,"]")])
dens = density(chains.burn.df.current[,paste0("shape_pomona[",i,"]")])
hpd.int.peak.titer = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
post.individual[which(post.individual$id==i & post.individual$model==model),"shape.mean.95lo"] = hpd.int.peak.titer[1]
post.individual[which(post.individual$id==i & post.individual$model==model),"shape.mean.95hi"] = hpd.int.peak.titer[2]
post.individual[which(post.individual$id==i & post.individual$model==model),"scale.mean"] = mean(chains.burn.df.current[,paste0("scale_pomona[",i,"]")])
dens = density(chains.burn.df.current[,paste0("scale_pomona[",i,"]")])
hpd.int.peak.titer = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
post.individual[which(post.individual$id==i & post.individual$model==model),"scale.mean.95lo"] = hpd.int.peak.titer[1]
post.individual[which(post.individual$id==i & post.individual$model==model),"scale.mean.95hi"] = hpd.int.peak.titer[2]
}
}
# calculate RMSE and adjust times of infection
if(model == 1) {
observed.data.for.fitting.single = observed.data.for.fitting
observed.data.for.fitting.single$time.since.infection = NA
observed.data.for.fitting.single$log.titer = observed.data.for.fitting.single$titer.pomona
for(i in 1:N.inds){
idx.current = which(observed.data.for.fitting.single$id == i)
observed.data.for.fitting.single$time.since.infection[idx.current] = observed.data.for.fitting.single$time[idx.current] - post.individual[which(post.individual$model==model & post.individual$id == i),"toi.mean"]
}
post.overall[model,"RMSE"] = round(RMSE.fun.linear.individual.decay(peak.titers = post.individual[which(post.individual$model==model),"peak.titer.mean"],slopes = post.individual[which(post.individual$model==model),"decay.rate.mean"], observed.data = observed.data.for.fitting.single) ,5)
}
if(model == 2) {
observed.data.for.fitting.double = observed.data.for.fitting
observed.data.for.fitting.double$time.since.infection = NA
observed.data.for.fitting.double$titer = observed.data.for.fitting.double$titer.pomona
for(i in 1:N.inds){
idx.current = which(observed.data.for.fitting.double$id == i)
observed.data.for.fitting.double$time.since.infection[idx.current] = observed.data.for.fitting.double$time[idx.current] - post.individual[which(post.individual$model==model & post.individual$id == i),"toi.mean"]
}
post.overall[model,"RMSE"] = round(RMSE.fun.double.exp.individual.decay(peak.titers = post.individual[which(post.individual$model==model),"peak.titer.mean"],decay.rate = -post.individual[which(post.individual$model==model),"decay.rate.mean"], observed.data = observed.data.for.fitting.double) ,5)
}
if(model == 3) {
observed.data.for.fitting.power = observed.data.for.fitting
observed.data.for.fitting.power$time.since.infection = NA
observed.data.for.fitting.power$titer = observed.data.for.fitting.power$titer.pomona
for(i in 1:N.inds){
idx.current = which(observed.data.for.fitting.power$id == i)
observed.data.for.fitting.power$time.since.infection[idx.current] = observed.data.for.fitting.power$time[idx.current] - post.individual[which(post.individual$model==model & post.individual$id == i),"toi.mean"]
}
post.overall[model,"RMSE"] = round(RMSE.fun.power.individual.decay(peak.titers = post.individual[which(post.individual$model==model),"peak.titer.mean"],shape = post.individual[which(post.individual$model==model),"shape.mean"],scale = post.individual[which(post.individual$model==model),"scale.mean"], observed.data = observed.data.for.fitting.power),5)
}
}
post.overall$gain[which(post.overall$model==1)] = mean(post.individual$toi.95.information.gained[which(post.individual$model==1)])
post.overall$gain[which(post.overall$model==2)] = mean(post.individual$toi.95.information.gained[which(post.individual$model==2)])
post.overall$gain[which(post.overall$model==3)] = mean(post.individual$toi.95.information.gained[which(post.individual$model==3)])
post.overall$model[1] = "Single exp"
post.overall$model[2] = "Double exp"
post.overall$model[3] = "Power"
post.individual$model[which(post.individual$model==1)] = "Single exp"
post.individual$model[which(post.individual$model==2)] = "Double exp"
post.individual$model[which(post.individual$model==3)] = "Power"
post.overall$model = factor(post.overall$model,levels = c("Single exp","Double exp","Power"))
post.individual$model = factor(post.individual$model,levels = c("Single exp","Double exp","Power"))
all.fun.post.overall = post.overall
kable(all.fun.post.overall ,"html") %>%
kable_styling(bootstrap_options = c("striped", "hover","condensed"),full_width = F)
# export posterior estimates
saveRDS(all.fun.post.overall,"Function_comparison_post_overall.RDS")
}
rm(chains.burn.df.1,chains.burn.df.2,chains.burn.df.3, chains.burn.df.current)
gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 2929202 156.5 5617298 300.0 NA 3812282 203.6
## Vcells 1087234541 8295.0 1812905625 13831.4 32768 1260268442 9615.13 Output
Double exponential function only.
3.0.1 Stats
chains.burn.df.loglik = chains.burn.df[,grep("LogLik",colnames(chains.burn.df))]
chains.burn.df.loglik = chains.burn.df.loglik[is.finite(rowSums(chains.burn.df.loglik)),]
loglik.all.matrix = as.matrix(chains.burn.df.loglik)
rm(chains.burn.df.loglik)
LOOIC.val = loo(loglik.all.matrix)
LOOIC.val.est = LOOIC.val$estimates["looic",][1]
peak.titer.individual.means.pomona = apply(chains.burn.df[,paste0("peak.titer.pomona.",uid)],MARGIN = 2,mean)
decay.rate.individual.means.pomona = apply(chains.burn.df[,paste0("decay.rate.pomona.",uid)],MARGIN = 2,mean)
observed.data.for.fitting.temp = observed.data.for.fitting
colnames(observed.data.for.fitting.temp)[4] = "titer"
RMSE.val.pomona = round(RMSE.fun.double.exp.individual.decay(peak.titers = peak.titer.individual.means.pomona,decay.rates = -decay.rate.individual.means.pomona, observed.data = observed.data.for.fitting.temp),3)
peak.titer.individual.means.aut = apply(chains.burn.df[,paste0("peak.titer.aut.",uid)],MARGIN = 2,mean)
decay.rate.individual.means.aut = apply(chains.burn.df[,paste0("decay.rate.aut.",uid)],MARGIN = 2,mean)
observed.data.for.fitting.temp = observed.data.for.fitting
colnames(observed.data.for.fitting.temp)[4] = "titer"
RMSE.val.aut = round(RMSE.fun.double.exp.individual.decay(peak.titers = peak.titer.individual.means.aut,decay.rates = -decay.rate.individual.means.aut, observed.data = observed.data.for.fitting.temp),3)| LOOIC value: 5052.9536887 Root mean square error (pomona): 2.435. Root mean square error (aut): 2.68. |
3.0.2 Lab effect
CDC vs Ithaca
dens = density(chains.burn.df$lab.effect)
lab.effect.mean = mean(chains.burn.df$lab.effect)
hpd.int.lab.effect = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
lab.fit = MASS::fitdistr(chains.burn.df$lab.effect,densfun = "normal")Antibody levels for samples (Pomona or Autumnalis) not tested at CDC are on average 0.5904501 (95% CrI 0.3885021-0.7942814; SD = 0.1030068) higher.
This is taken into account when fitting all model parameters.
plot.lab.effect.1 = ggplot(data=chains.burn.df,aes(x=lab.effect)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=lab.effect.mean,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.lab.effect[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.lab.effect[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Lab effect") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot.lab.effect.2 = plot.MCMC.chains(input.data = chains.burn.df.original, column.to.plot = which(colnames(chains.burn.df)=="lab.effect"),y.axis.label = "Lab effect",thinning=10)
plot.lab.effect.1
plot.lab.effect.2
3.0.3 Pomona
3.0.3.1 Posterior estimate mean peak titer:
dens = density(chains.burn.df$peak.titer.overall.pomona)
hpd.int.peak.titer.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
peak.titer.overall.maxdens.pomona=round(dens$x[which.max(dens$y)],2)
peak.titer.overall.mean.pomona = mean(chains.burn.df$peak.titer.overall.pomona)
peak.titer.overall.median.pomona = median(chains.burn.df$peak.titer.overall.pomona)
dens = density(chains.burn.df$peak.titer.sd.overall.pomona)
hpd.int.peak.titer.sd.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
peak.titer.sd.overall.maxdens.pomona=round(dens$x[which.max(dens$y)],2)
peak.titer.sd.overall.mean.pomona = mean(chains.burn.df$peak.titer.sd.overall.pomona)
peak.titer.sd.overall.median.pomona = median(chains.burn.df$peak.titer.sd.overall.pomona)plot1 = ggplot(data=chains.burn.df,aes(x=peak.titer.overall.pomona)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=peak.titer.overall.maxdens.pomona,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.peak.titer.pomona[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.peak.titer.pomona[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Peak antibody level mean (Pomona) \n(log2 dilution)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot2 = plot.MCMC.chains(input.data = chains.burn.df.original, column.to.plot = which(colnames(chains.burn.df)=="peak.titer.overall.pomona"),y.axis.label = "Peak antibody level mean (Pomona) \n(log2 dilution)",thinning=10)
plot1
plot2
- Posterior estimate (max dens): 6.96
- Posterior estimate (mean): 6.9725804
- Posterior estimate (median): 6.9720479
- Highest density credible interval = 6.699767 - 7.2470921
3.0.3.2 Posterior estimate standard deviation of peak titer:
plot1 = ggplot(data=chains.burn.df,aes(x=peak.titer.sd.overall.pomona)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=peak.titer.sd.overall.maxdens.pomona,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.peak.titer.sd.pomona[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.peak.titer.sd.pomona[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Peak antibody level SD (Pomona) \n(log2 dilution)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot2 = plot.MCMC.chains(input.data = chains.burn.df.original, column.to.plot = which(colnames(chains.burn.df)=="peak.titer.sd.overall.pomona"), y.axis.label = "Peak antibody level SD (Pomona) \n(log2 dilution)",thinning=10)
plot1
plot2
- Posterior estimate (max dens): 1.82
- Posterior estimate (mean): 1.8431128
- Posterior estimate (median): 1.8409946
- Highest density credible interval = 1.6153003 - 2.0719977
3.0.3.3 Peak titer distribution using the posterior mean estimates of peak titer mean and SD
Including 200 random draws from the posteriors:
Nsamp = 200
means = sample(chains.burn.df$peak.titer.overall.pomona,Nsamp,replace = F)
sds = sample(chains.burn.df$peak.titer.sd.overall.pomona,Nsamp,replace = F)
ggplot(data = data.frame(x = 0:15),aes(x=x)) +
scale_x_continuous(breaks=0:15) +
mapply(function(mean,sd){
stat_function(fun = dnorm, colour = brewer.pal(11,"Spectral")[10],alpha=0.5,args = list(mean = mean,sd = sd),size=0.3)
},
mean = means,
sd = sds
) +
stat_function(fun = dnorm, colour = brewer.pal(11,"Spectral")[1],args = list(mean = peak.titer.overall.mean.pomona,sd = peak.titer.sd.overall.mean.pomona),size=1.5) +
xlab("Peak antibody level (Pomona) \n(log2 dilution)") +
ylab("Posterior distribution") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
)
3.0.3.4 Posterior estimate mean decay rate:
dens = density(chains.burn.df$decay.rate.overall.pomona)
hpd.int.decay.rate.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
decay.rate.overall.maxdens.pomona=round(dens$x[which.max(dens$y)],6)
decay.rate.overall.mean.pomona = mean(chains.burn.df$decay.rate.overall.pomona)
decay.rate.overall.median.pomona = median(chains.burn.df$decay.rate.overall.pomona)
dens = density(chains.burn.df$decay.rate.sd.overall.pomona)
hpd.int.decay.rate.sd.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
decay.rate.sd.overall.maxdens.pomona=round(dens$x[which.max(dens$y)],6)
decay.rate.sd.overall.mean.pomona = mean(chains.burn.df$decay.rate.sd.overall.pomona)
decay.rate.sd.overall.median.pomona = median(chains.burn.df$decay.rate.sd.overall.pomona)plot1 = ggplot(data=chains.burn.df,aes(x=decay.rate.overall.pomona)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=decay.rate.overall.mean.pomona,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.decay.rate.pomona[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.decay.rate.pomona[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Decay rate mean (Pomona) \n(1/day)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot2 = plot.MCMC.chains(input.data = chains.burn.df.original, column.to.plot = which(colnames(chains.burn.df)=="decay.rate.overall.pomona"), y.axis.label = "Decay rate mean (Pomona) \n(1/day)",thinning=10)
plot1
plot2
- Posterior estimate (max dens): 8.58^{-4}
- Posterior estimate (mean): 8.6091115^{-4}
- Posterior estimate (median): 8.5991881^{-4}
- Highest density credible interval = 7.8285993^{-4} - 9.4081133^{-4}
3.0.3.5 Posterior estimate standard deviation of decay rate:
plot1 = ggplot(data=chains.burn.df,aes(x=decay.rate.sd.overall.pomona)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=decay.rate.sd.overall.mean.pomona,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.decay.rate.sd.pomona[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.decay.rate.sd.pomona[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Decay rate SD (Pomona) \n(1/day)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot2 = plot.MCMC.chains(input.data = chains.burn.df.original, column.to.plot = which(colnames(chains.burn.df)=="decay.rate.sd.overall.pomona"), y.axis.label = "Decay rate SD (Pomona) \n(1/day)",thinning=10)
plot1
plot2
- Posterior estimate (max dens): 3.46^{-4}
- Posterior estimate (mean): 3.4935971^{-4}
- Posterior estimate (median): 3.481226^{-4}
- Highest density credible interval = 2.7815012^{-4} - 4.2372114^{-4}
3.0.3.6 Decay rate distribution using the posterior mean estimates of peak titer mean and SD
Including 200 random draws from the posteriors:
Nsamp = 200
means = sample(chains.burn.df$decay.rate.overall.pomona,Nsamp,replace = F)
sds = sample(chains.burn.df$decay.rate.sd.overall.pomona,Nsamp,replace = F)
ggplot(data = data.frame(x = seq(-0.0002,0.0019,0.00001)),aes(x=x)) +
mapply(function(mean,sd){
stat_function(fun = dnorm, colour = brewer.pal(11,"Spectral")[10],alpha=0.5,args = list(mean = mean,sd = sd),size=0.3)
},
mean = means,
sd = sds
) +
stat_function(fun = dnorm, colour = brewer.pal(11,"Spectral")[1],args = list(mean = decay.rate.overall.mean.pomona,sd = decay.rate.sd.overall.mean.pomona),size=1.5) +
xlab("Decay rate (Pomona) \n(1/day)") +
ylab("Posterior distribution") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
)
3.0.3.7 Joint posterior peak titer vs decay means
Population level means:
joint.dens = chains.burn.df[,c("decay.rate.overall.pomona","peak.titer.overall.pomona")]
joint.dens$dens = get_density(chains.burn.df$decay.rate.overall.pomona,chains.burn.df$peak.titer.overall.pomona,n = 80)
joint.plot = ggplot(joint.dens,aes(x = decay.rate.overall.pomona, y = peak.titer.overall.pomona,color=dens)) +
geom_point(alpha = 0.4,size = 0.5) +
scale_colour_gradientn(colors = rev(brewer.pal(n = 6,"Spectral")),name="Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 14),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")) +
xlab("Decay rate posterior mean (Pomona) \n(1/day)") +
ylab("Peak antibody level \n posterior mean (Pomona) \n(log2 dilution)")
joint.plot
Individual posterior means:
# 1200 posterior values per individual
n.it.each = 200
ind.means = data.frame(id = rep(1:N.inds,each = n.it.each*6),
peak.titer.pomona = NA,
peak.titer.aut = NA,
decay.rate.pomona = NA,
decay.rate.aut = NA,
iteration = NA)
ind.means.2 = data.frame(id = 1:N.inds,
peak.titer.pomona = NA,
peak.titer.aut = NA,
decay.rate.pomona = NA,
decay.rate.aut = NA)
for(i in 1:N.inds){
ind.means$peak.titer.pomona[which(ind.means$id == i)] = chains.burn.df[which(chains.burn.df$iteration == tail(chains.burn.df$iteration,n.it.each)),paste0("peak.titer.pomona.",i)]
ind.means$peak.titer.aut[which(ind.means$id == i)] = chains.burn.df[which(chains.burn.df$iteration == tail(chains.burn.df$iteration,n.it.each)),paste0("peak.titer.aut.",i)]
ind.means$decay.rate.pomona[which(ind.means$id == i)] = chains.burn.df[which(chains.burn.df$iteration == tail(chains.burn.df$iteration,n.it.each)),paste0("decay.rate.pomona.",i)]
ind.means$decay.rate.aut[which(ind.means$id == i)] = chains.burn.df[which(chains.burn.df$iteration == tail(chains.burn.df$iteration,n.it.each)),paste0("decay.rate.aut.",i)]
ind.means$iteration = chains.burn.df[which(chains.burn.df$iteration == tail(chains.burn.df$iteration,n.it.each)),"iteration"]
ind.means.2$peak.titer.pomona[which(ind.means.2$id == i)] = mean(chains.burn.df[,paste0("peak.titer.pomona.",i)])
ind.means.2$peak.titer.aut[which(ind.means.2$id == i)] = mean(chains.burn.df[,paste0("peak.titer.aut.",i)])
ind.means.2$decay.rate.pomona[which(ind.means.2$id == i)] = mean(chains.burn.df[,paste0("decay.rate.pomona.",i)])
ind.means.2$decay.rate.aut[which(ind.means.2$id == i)] = mean(chains.burn.df[,paste0("decay.rate.aut.",i)])
}
ind.means$dens = get_density(ind.means$peak.titer.pomona,ind.means$decay.rate.pomona,n = 80)
ggplot(ind.means,aes(x = decay.rate.pomona, y = peak.titer.pomona,color=dens)) +
geom_point(alpha = 0.4,size = 0.5) +
scale_colour_gradientn(colors = rev(brewer.pal(n = 6,"Spectral")),name="Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
#legend.position = c(0.78,0.85),
legend.text = element_text(size=10),
legend.title = element_text(size=11),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
) +
xlab("Decay rate posterior samples \n(1/day)") +
ylab("Peak antibody level posterior samples \n(log2 dilution)")
joint.plot.peak.decay = ggplot(ind.means,aes(x = decay.rate.pomona, y = peak.titer.pomona,color=dens)) +
geom_point(alpha = 0.4,size = 0.5) +
scale_colour_gradientn(colors = rev(brewer.pal(n = 6,"Spectral")),name="Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
#legend.position = c(0.78,0.85),
#legend.position = "none",
legend.text = element_text(size=10),
legend.title = element_text(size=11),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
) +
xlab("Decay rate posterior samples \n(1/day)") +
ylab("Peak antibody level \n posterior samples \n(log2 dilution)") +
labs(tag="(C)")Correlation:
n.it = 400
posterior.cor = data.frame(iteration = rep(tail(unique(chains.burn.df$iteration),n.it),6),
chain = rep(1:6,each = n.it),
slope = NA,
p.val = NA,
adj.r2 = NA)
for(i in 1:nrow(posterior.cor)){
cur.dat = data.frame(peak.titer = reshape2::melt(chains.burn.df[which(chains.burn.df$iteration==posterior.cor$iteration[i] & chains.burn.df$chain == posterior.cor$chain[i]),paste0("peak.titer.pomona.",1:N.inds)])[,2],
decay.rate = reshape2::melt(chains.burn.df[which(chains.burn.df$iteration==posterior.cor$iteration[i] & chains.burn.df$chain == posterior.cor$chain[i]),paste0("decay.rate.pomona.",1:N.inds)])[,2]
)
cur.lm = summary(lm(peak.titer~decay.rate,data = cur.dat))
posterior.cor$slope[i] = cur.lm$coefficients[2]
posterior.cor$p.val[i] = round(cur.lm$coefficients[8],5)
posterior.cor$adj.r2[i] = cur.lm$adj.r.squared
}
dens = density(posterior.cor$p.val)
hpd.int.p.val.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
dens = density(posterior.cor$slope)
hpd.int.slope.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
dens = density(posterior.cor$adj.r2)
hpd.int.adj.r2.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F) Median p value: 0.512485 (95% CrI 0.0226156-0.9959449.
Proportion of P values below 0.05: 0.5136561
Mean slope: 91.0964541 (95% CrI -492.3092912-636.2949554.
Median adjusted R^2: -0.0018662 (95% CrI -0.00441-0.0081118.
ggplot(data=posterior.cor,aes(x=slope)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=hpd.int.slope.pomona[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.slope.pomona[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Slope decay rate vs peak antibody level") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
ggplot(data=posterior.cor,aes(x=p.val)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=hpd.int.p.val.pomona[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.p.val.pomona[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("P value regression decay rate vs peak antibody level") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
ggplot(data=posterior.cor,aes(x=adj.r2)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=mean(posterior.cor$adj.r2),col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.adj.r2.pomona[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.adj.r2.pomona[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Adj. R^2 decay rate vs peak antibody level") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
Using one posterior mean for each individual:
ggplot(ind.means.2,aes(x = decay.rate.pomona, y = peak.titer.pomona)) +
geom_point(alpha = 1,size = 0.8,col = brewer.pal(n = 11,"Spectral")[10]) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 14),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")) +
xlab("Decay rate posterior \nindividual means (Pomona) \n(1/day)") +
ylab("Peak antibody level \nposterior individual means (Pomona) \n(log2 dilution)")
Correlation using posterior means:
lm1 = lm(peak.titer.pomona~decay.rate.pomona,data = ind.means.2)
# plot(lm1)
summary(lm1)##
## Call:
## lm(formula = peak.titer.pomona ~ decay.rate.pomona, data = ind.means.2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2254 -1.0340 0.0666 1.0380 4.4835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.0982 0.3239 25.00 < 2e-16 ***
## decay.rate.pomona -1307.7614 362.3005 -3.61 0.000358 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.53 on 305 degrees of freedom
## Multiple R-squared: 0.04097, Adjusted R-squared: 0.03782
## F-statistic: 13.03 on 1 and 305 DF, p-value: 0.0003583Two functions taken from the predicted values of the regression model:
exp.dat.1 = data.frame(x = 1:3000,y = exp.fun(start.titer = 7.5,rate = -0.0004,time = 1:3000))
exp.dat.2 = data.frame(x = 1:3000,y = exp.fun(start.titer = 6.5,rate = -0.0011,time = 1:3000))
plot(1:3000,exp.dat.1$y,type="l",ylim = c(0,8))
lines(1:3000,exp.dat.2$y,col="red")
3.0.3.8 Fitted function
(red = estimated function)
fit.dat = exp.fun(start.titer = peak.titer.overall.mean.pomona, rate = -decay.rate.overall.mean.pomona, time = 0:max(observed.data.for.fitting$time))
fit.dat = data.frame(titer = fit.dat, time = 0:max(observed.data.for.fitting$time))
ggplot() +
geom_line(data = observed.data.for.fitting,aes(x = time,y = titer.pomona,col = id,group = id),alpha = 0.4,size = 0.6) +
geom_point(data = observed.data.for.fitting,aes(x = time,y = titer.pomona,col = id,group = id),size=1.2,alpha = 0.9) +
geom_line(data=fit.dat,aes(x = time, y = titer),col=brewer.pal(11,"Spectral")[1],size=1.3) +
scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since first positive sample (days)") +
ylab("Antibody level (Pomona) \n(log2 dilution)")
Time = time since peak antibody level
fit.dat.pomona = exp.fun(start.titer = peak.titer.overall.mean.pomona, rate = -decay.rate.overall.mean.pomona, time = 0:max(observed.data.for.fitting$time.since.peak))
fit.dat.pomona = data.frame(titer = fit.dat.pomona, time = 0:max(observed.data.for.fitting$time.since.peak))
ggplot() +
geom_line(data = observed.data.for.fitting,aes(x = time.since.peak,y = titer.pomona,col = id,group = id),alpha = 0.4,size = 0.6) +
geom_point(data = observed.data.for.fitting,aes(x = time.since.peak,y = titer.pomona,col = id,group = id),size=1.2,alpha = 0.9) +
geom_line(data=fit.dat.pomona,aes(x = time, y = titer),col=brewer.pal(11,"Spectral")[1],size=1.3) +
scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (Pomona) \n(log2 dilution)")
Including fitted functions for 200 random samples from the posterior distribution (within 95% credible interval):
# sample N values from posterior distribution to get sense of error
N.samp = 200
set.seed(1234);sampled.iterations = sample(which(chains.burn.df$peak.titer.overall.pomona > hpd.int.peak.titer.pomona[1] & chains.burn.df$peak.titer.overall.pomona < hpd.int.peak.titer.pomona[2]), size = N.samp, replace = T)
peak.titer.samp.mean.pomona = chains.burn.df$peak.titer.overall.pomona[sampled.iterations]
peak.titer.samp.sd.pomona = chains.burn.df$peak.titer.sd.overall.pomona[sampled.iterations]
peak.titer.samp.df.pomona = data.frame(peak.titer.samp.mean.pomona,peak.titer.samp.sd.pomona)
peak.titer.samp.fun = function(x) rnorm(1,x[1],x[2])
peak.titer.samp.pomona = apply(peak.titer.samp.df.pomona,1,peak.titer.samp.fun)
decay.rate.samp.mean.pomona = chains.burn.df$decay.rate.overall.pomona[sampled.iterations]
decay.rate.samp.pomona = mean(chains.burn.df$decay.rate.overall.pomona)
plot.times = 0:max(observed.data.for.fitting$time.since.peak)
fit.dat.cloud.pomona = data.frame(time = rep(plot.times,N.samp), samp = rep(1:N.samp,each = length(plot.times)), titer = NA)
for(i in 1:N.samp){
fit.dat.cloud.pomona$titer[which(fit.dat.cloud.pomona$samp == i)] = exp.fun(start.titer = peak.titer.samp.pomona[i], rate = -decay.rate.samp.pomona, time = plot.times)
}
ggplot() +
geom_line(data=fit.dat.cloud.pomona, aes(x = time, y = titer, group = samp), col = brewer.pal(11,"Spectral")[10],alpha=0.4,size=0.3) +
geom_line(data=fit.dat.pomona,aes(x = time, y = titer),col=brewer.pal(11,"Spectral")[1],size=1.5) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (Pomona) \n(log2 dilution)")
3.0.4 Autumnalis
3.0.4.1 Posterior estimate mean peak titer:
dens = density(chains.burn.df$peak.titer.overall.aut)
hpd.int.peak.titer.aut = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
peak.titer.overall.maxdens.aut=round(dens$x[which.max(dens$y)],2)
peak.titer.overall.mean.aut = mean(chains.burn.df$peak.titer.overall.aut)
peak.titer.overall.median.aut = median(chains.burn.df$peak.titer.overall.aut)
dens = density(chains.burn.df$peak.titer.sd.overall.aut)
hpd.int.peak.titer.sd.aut = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
peak.titer.sd.overall.maxdens.aut=round(dens$x[which.max(dens$y)],2)
peak.titer.sd.overall.mean.aut = mean(chains.burn.df$peak.titer.sd.overall.aut)
peak.titer.sd.overall.median.aut = median(chains.burn.df$peak.titer.sd.overall.aut)plot1 = ggplot(data=chains.burn.df,aes(x=peak.titer.overall.aut)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=peak.titer.overall.maxdens.aut,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.peak.titer.aut[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.peak.titer.aut[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Peak antibody level mean (Autumnalis) \n(log2 dilution)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot2 = plot.MCMC.chains(input.data = chains.burn.df, column.to.plot = which(colnames(chains.burn.df)=="peak.titer.overall.aut"),y.axis.label = "Peak antibody level mean (Autumnalis) \n(log2 dilution)",thinning=10)
plot1
plot2
- Posterior estimate (max dens): 7.45
- Posterior estimate (mean): 7.4654311
- Posterior estimate (median): 7.4635202
- Highest density credible interval = 7.095945 - 7.8322631
3.0.4.2 Posterior estimate standard deviation of peak titer:
plot1 = ggplot(data=chains.burn.df,aes(x=peak.titer.sd.overall.aut)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=peak.titer.sd.overall.maxdens.aut,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.peak.titer.sd.aut[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.peak.titer.sd.aut[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Peak antibody level SD (Autumnalis) \n(log2 dilution)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot2 = plot.MCMC.chains(input.data = chains.burn.df, column.to.plot = which(colnames(chains.burn.df)=="peak.titer.sd.overall.aut"), y.axis.label = "Peak antibody level SD (Autumnalis) \n(log2 dilution)",thinning=10)
plot1
plot2
- Posterior estimate (max dens): 2.08
- Posterior estimate (mean): 2.0984924
- Posterior estimate (median): 2.0947261
- Highest density credible interval = 1.8159382 - 2.3866797
3.0.4.3 Peak titer distribution using the posterior mean estimates of peak titer mean and SD
Including 200 random draws from the posteriors:
Nsamp = 200
means = sample(chains.burn.df$peak.titer.overall.aut,Nsamp,replace = F)
sds = sample(chains.burn.df$peak.titer.sd.overall.aut,Nsamp,replace = F)
ggplot(data = data.frame(x = 0:15),aes(x=x)) +
scale_x_continuous(breaks=0:15) +
mapply(function(mean,sd){
stat_function(fun = dnorm, colour = brewer.pal(11,"Spectral")[10],alpha=0.5,args = list(mean = mean,sd = sd),size=0.3)
},
mean = means,
sd = sds
) +
stat_function(fun = dnorm, colour = brewer.pal(11,"Spectral")[1],args = list(mean = peak.titer.overall.mean.aut,sd = peak.titer.sd.overall.mean.aut),size=1.5) +
xlab("Peak antibody level (Autumnalis) \n(log2 dilution)") +
ylab("Posterior distribution") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
)
3.0.4.4 Posterior estimate mean decay rate:
dens = density(chains.burn.df$decay.rate.overall.aut)
hpd.int.decay.rate.aut = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
decay.rate.overall.maxdens.aut=round(dens$x[which.max(dens$y)],6)
decay.rate.overall.mean.aut = mean(chains.burn.df$decay.rate.overall.aut)
decay.rate.overall.median.aut = median(chains.burn.df$decay.rate.overall.aut)
dens = density(chains.burn.df$decay.rate.sd.overall.aut)
hpd.int.decay.rate.sd.aut = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
decay.rate.sd.overall.maxdens.aut=round(dens$x[which.max(dens$y)],6)
decay.rate.sd.overall.mean.aut = mean(chains.burn.df$decay.rate.sd.overall.aut)
decay.rate.sd.overall.median.aut = median(chains.burn.df$decay.rate.sd.overall.aut)plot1 = ggplot(data=chains.burn.df,aes(x=decay.rate.overall.aut)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=decay.rate.overall.mean.aut,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.decay.rate.aut[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.decay.rate.aut[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Decay rate mean (Autumnalis) \n(1/day)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot2 = plot.MCMC.chains(input.data = chains.burn.df, column.to.plot = which(colnames(chains.burn.df)=="decay.rate.overall.aut"), y.axis.label = "Decay rate mean (Autumnalis) \n(1/day)",thinning=10)
plot1
plot2
- Posterior estimate (max dens): 6.54^{-4}
- Posterior estimate (mean): 6.5954534^{-4}
- Posterior estimate (median): 6.583367^{-4}
- Highest density credible interval = 5.6526982^{-4} - 7.5483711^{-4}
3.0.4.5 Posterior estimate standard deviation of decay rate:
plot1 = ggplot(data=chains.burn.df,aes(x=decay.rate.sd.overall.aut)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept=decay.rate.sd.overall.mean.aut,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept=hpd.int.decay.rate.sd.aut[1],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept=hpd.int.decay.rate.sd.aut[2],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
xlab("Decay rate SD (Autumnalis) \n(1/day)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
plot2 = plot.MCMC.chains(input.data = chains.burn.df, column.to.plot = which(colnames(chains.burn.df)=="decay.rate.sd.overall.aut"), y.axis.label = "Decay rate SD (Autumnalis) \n(1/day)",thinning=10)
plot1
plot2
- Posterior estimate (max dens): 2.7^{-4}
- Posterior estimate (mean): 2.7441069^{-4}
- Posterior estimate (median): 2.731166^{-4}
- Highest density credible interval = 1.878479^{-4} - 3.6263849^{-4}
3.0.4.6 Decay rate distribution using the posterior mean estimates of peak titer mean and SD
Including 200 random draws from the posteriors:
Nsamp = 200
means = sample(chains.burn.df$decay.rate.overall.aut,Nsamp,replace = F)
sds = sample(chains.burn.df$decay.rate.sd.overall.aut,Nsamp,replace = F)
ggplot(data = data.frame(x = seq(-0.0002,0.0016,0.00001)),aes(x=x)) +
mapply(function(mean,sd){
stat_function(fun = dnorm, colour = brewer.pal(11,"Spectral")[10],alpha=0.5,args = list(mean = mean,sd = sd),size=0.3)
},
mean = means,
sd = sds
) +
stat_function(fun = dnorm, colour = brewer.pal(11,"Spectral")[1],args = list(mean = decay.rate.overall.mean.aut,sd = decay.rate.sd.overall.mean.aut),size=1.5) +
xlab("Decay rate (Autumnalis) \n(1/day)") +
ylab("Posterior distribution") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
)
3.0.4.7 Joint posterior peak titer vs decay means
joint.dens = chains.burn.df[,c("decay.rate.overall.aut","peak.titer.overall.aut")]
joint.dens$dens = get_density(chains.burn.df$decay.rate.overall.aut,chains.burn.df$peak.titer.overall.aut,n = 80)
joint.plot = ggplot(joint.dens,aes(x = decay.rate.overall.aut, y = peak.titer.overall.aut,color=dens)) +
geom_point(alpha = 0.4,size = 0.5) +
scale_colour_gradientn(colors = rev(brewer.pal(n = 6,"Spectral")),name="Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 14),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")) +
xlab("Decay rate posterior mean (Autumnalis) \n(1/day)") +
ylab("Peak titer posterior mean (Autumnalis) \n(log2 dilution)")
joint.plot
Individual posterior means:
ind.means$dens = get_density(ind.means$peak.titer.aut,ind.means$decay.rate.aut,n = 80)
joint.plot = ggplot(ind.means,aes(x = decay.rate.aut, y = peak.titer.aut,color=dens)) +
geom_point(alpha = 0.4,size = 0.5) +
scale_colour_gradientn(colors = rev(brewer.pal(n = 6,"Spectral")),name="Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 14),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")) +
xlab("Decay rate posterior individual means (Autumnalis) \n(1/day)") +
ylab("Peak antibody level \n posterior individual means (Autumnalis) \n(log2 dilution)")
joint.plot
ggplot(ind.means.2,aes(x = decay.rate.aut, y = peak.titer.aut)) +
geom_point(alpha = 1,size = 0.8,col = brewer.pal(n = 11,"Spectral")[10]) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 14),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")) +
xlab("Decay rate posterior individual means (Autumnalis) \n(1/day)") +
ylab("Peak antibody level \n posterior individual means (Autumnalis) \n(log2 dilution)")
(points on one line = no Autumnalis sample available, so sampling
from the prior.
variation along peak titer axis is due to the correlation between Pomona
and Autumnalis peak level)
3.0.4.8 Fitted function
(red = estimated function)
fit.dat.aut = exp.fun(start.titer = peak.titer.overall.mean.aut, rate = -decay.rate.overall.mean.aut, time = 0:max(observed.data.for.fitting$time))
fit.dat.aut = data.frame(titer = fit.dat.aut, time = 0:max(observed.data.for.fitting$time))
ggplot() +
geom_line(data = observed.data.for.fitting,aes(x = time,y = titer.aut,col = id,group = id),alpha = 0.4,size = 0.6) +
geom_point(data = observed.data.for.fitting,aes(x = time,y = titer.aut,col = id,group = id),size=1.2,alpha = 0.9) +
geom_line(data=fit.dat.aut,aes(x = time, y = titer),col=brewer.pal(11,"Spectral")[1],size=1.3) +
scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since first positive sample (days)") +
ylab("Antibody level (Autumnalis) \n(log2 dilution)")
Time = time since peak antibody level
fit.dat.aut = exp.fun(start.titer = peak.titer.overall.mean.aut, rate = -decay.rate.overall.mean.aut, time = 0:max(observed.data.for.fitting$time.since.peak))
fit.dat.aut = data.frame(titer = fit.dat.aut, time = 0:max(observed.data.for.fitting$time.since.peak))
ggplot() +
geom_line(data = observed.data.for.fitting,aes(x = time.since.peak,y = titer.aut,col = id,group = id),alpha = 0.4,size = 0.6) +
geom_point(data = observed.data.for.fitting,aes(x = time.since.peak,y = titer.aut,col = id,group = id),size=1.2,alpha = 0.9) +
geom_line(data=fit.dat.aut,aes(x = time, y = titer),col=brewer.pal(11,"Spectral")[1],size=1.3) +
scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (Autumnalis) \n(log2 dilution)")
Including fitted functions for 200 random samples from the posterior distribution (within 95% credible interval):
# sample N values from posterior distribution to get sense of error
N.samp = 200
set.seed(1234);sampled.iterations = sample(which(chains.burn.df$peak.titer.overall.aut > hpd.int.peak.titer.aut[1] & chains.burn.df$peak.titer.overall.aut < hpd.int.peak.titer.aut[2]), size = N.samp, replace = T)
peak.titer.samp.mean.aut = chains.burn.df$peak.titer.overall.aut[sampled.iterations]
peak.titer.samp.sd.aut = chains.burn.df$peak.titer.sd.overall.aut[sampled.iterations]
peak.titer.samp.df.aut = data.frame(peak.titer.samp.mean.aut,peak.titer.samp.sd.aut)
peak.titer.samp.fun = function(x) rnorm(1,x[1],x[2])
peak.titer.samp.aut = apply(peak.titer.samp.df.aut,1,peak.titer.samp.fun)
decay.rate.samp.mean.aut = chains.burn.df$decay.rate.overall.aut[sampled.iterations]
decay.rate.samp.aut = mean(chains.burn.df$decay.rate.overall.aut)
plot.times = 0:max(observed.data.for.fitting$time.since.peak)
fit.dat.cloud.aut = data.frame(time = rep(plot.times,N.samp), samp = rep(1:N.samp,each = length(plot.times)), titer = NA)
for(i in 1:N.samp){
fit.dat.cloud.aut$titer[which(fit.dat.cloud.aut$samp == i)] = exp.fun(start.titer = peak.titer.samp.aut[i], rate = -decay.rate.samp.aut, time = plot.times)
}
ggplot() +
geom_line(data=fit.dat.cloud.aut, aes(x = time, y = titer, group = samp), col = brewer.pal(11,"Spectral")[10],alpha=0.4,size=0.3) +
geom_line(data=fit.dat.aut,aes(x = time, y = titer),col=brewer.pal(11,"Spectral")[1],size=1.5) +
scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (Autumnalis) \n(log2 dilution)")
3.1 Pomona and Autumnalis
3.1.1 Peak antibody level
Correlation between Pomona and Autumnalis peak antibody levels
r2.fun = function(x) {
summary(lm(as.numeric(x[1:(length(x)/2)])~as.numeric(x[((length(x)/2)+1):length(x)])))$r.squared
}
id.aut.not.all.na = unique(observed.data.for.fitting$id[which(!is.na(observed.data.for.fitting$titer.aut))])
r2s = apply(chains.burn.df[which(chains.burn.df$iteration %in% tail(chains.burn.df$iteration,1000)),c(paste0("peak.titer.aut.",id.aut.not.all.na),paste0("peak.titer.pomona.",id.aut.not.all.na))],1,r2.fun)
dens = density(r2s)
hpd.int.r2s = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
maxdens.r2s=round(dens$x[which.max(dens$y)],2)
intercept.fun = function(x) {
summary(lm(as.numeric(x[1:(length(x)/2)])~as.numeric(x[((length(x)/2)+1):length(x)])))$coefficients[1]
}
intercepts = apply(chains.burn.df[which(chains.burn.df$iteration %in% tail(chains.burn.df$iteration,1000)),c(paste0("peak.titer.aut.",id.aut.not.all.na),paste0("peak.titer.pomona.",id.aut.not.all.na))],1,intercept.fun)
dens = density(intercepts)
hpd.int.intercepts = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
maxdens.intercepts=round(dens$x[which.max(dens$y)],2)
slope.fun = function(x) {
summary(lm(as.numeric(x[1:(length(x)/2)])~as.numeric(x[((length(x)/2)+1):length(x)])))$coefficients[2]
}
slopes = apply(chains.burn.df[which(chains.burn.df$iteration %in% tail(chains.burn.df$iteration,1000)),c(paste0("peak.titer.aut.",id.aut.not.all.na),paste0("peak.titer.pomona.",id.aut.not.all.na))],1,slope.fun)
dens = density(slopes)
hpd.int.slopes = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
maxdens.slopes=round(dens$x[which.max(dens$y)],2)R^2 = 0.98, 95% CrI = 0.8666093 - 1.0013974.
Intercept = -0.18, 95% CrI = -1.4496327 - 0.8999058.
(= Aut peak level - Pom peak level)
Effect estimate = 1.1, 95% CrI = 0.9450365 - 1.2603133.
(= slope)
Nsamp = 200
means.pomona = sample(chains.burn.df$peak.titer.overall.pomona,Nsamp,replace = F)
sds.pomona = sample(chains.burn.df$peak.titer.sd.overall.pomona,Nsamp,replace = F)
means.aut = sample(chains.burn.df$peak.titer.overall.aut,Nsamp,replace = F)
sds.aut = sample(chains.burn.df$peak.titer.sd.overall.aut,Nsamp,replace = F)
ggplot(data = data.frame(x = 0:15),aes(x=x)) +
scale_x_continuous(breaks=0:15) +
mapply(function(mean,sd){
stat_function(fun = dnorm, args = list(mean = mean,sd = sd),aes(color="Autumnalis"),size=0.4)
},
mean = means.aut,
sd = sds.aut
) +
stat_function(fun = dnorm, args = list(mean = peak.titer.overall.mean.aut,sd = peak.titer.sd.overall.mean.aut),color = brewer.pal(11,"Spectral")[1],size=1.5) +
geom_vline(xintercept=peak.titer.overall.mean.aut,col=brewer.pal(11,"Spectral")[1],size=0.8,alpha=0.6) +
mapply(function(mean,sd){
stat_function(fun = dnorm,args = list(mean = mean,sd = sd),aes(color="Pomona"),size=0.4)
},
mean = means.pomona,
sd = sds.pomona
) +
stat_function(fun = dnorm, args = list(mean = peak.titer.overall.mean.pomona,sd = peak.titer.sd.overall.mean.pomona),color = brewer.pal(11,"Spectral")[10],size=1.5) +
geom_vline(xintercept=peak.titer.overall.mean.pomona,col=brewer.pal(11,"Spectral")[10],size=0.8,alpha=0.6) +
scale_color_manual("Serovar",values = alpha(c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1]),0.3)) +
xlab("Peak antibody level \n(log2 dilution)") +
ylab("Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = c(0.85,0.75),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent")
)
plot1 = ggplot(data = data.frame(x = 0:15),aes(x=x)) +
scale_x_continuous(breaks=seq(0,15,2),limits = c(0,16)) +
mapply(function(mean,sd){
stat_function(fun = dnorm, args = list(mean = mean,sd = sd),aes(color="Autumnalis"),size=0.2,alpha=0.1)
},
mean = means.aut,
sd = sds.aut
) +
mapply(function(mean,sd){
stat_function(fun = dnorm,args = list(mean = mean,sd = sd),aes(color="Pomona"),size=0.2,alpha=0.1)
},
mean = means.pomona,
sd = sds.pomona
) +
stat_function(fun = dnorm, args = list(mean = peak.titer.overall.mean.aut,sd = peak.titer.sd.overall.mean.aut),color = brewer.pal(11,"Spectral")[1],size=1.5) +
stat_function(fun = dnorm, args = list(mean = peak.titer.overall.mean.pomona,sd = peak.titer.sd.overall.mean.pomona),color = brewer.pal(11,"Spectral")[10],size=1.5) +
#geom_vline(xintercept=peak.titer.overall.mean.aut,col=brewer.pal(11,"Spectral")[1],size=1,alpha=1) +
#geom_vline(xintercept=peak.titer.overall.mean.pomona,col=brewer.pal(11,"Spectral")[10],size=1,alpha=1) +
scale_color_manual("Serovar",values = alpha(c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1]),0.3)) +
xlab("Peak antibody level \n(log2 dilution)") +
ylab("Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.title = element_text(size = 10),
legend.text = element_text(size = 9),
#legend.position = c(0.78,0.85),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
) +
labs(tag = "(A)")Individual level means:
cor.plot.individual = ggplot(ind.means.2,aes(x = peak.titer.pomona, y = peak.titer.aut)) +
geom_point(alpha = 1,size = 0.8,col = brewer.pal(n = 11,"Spectral")[10]) +
theme_light(base_family = "Avenir Next") +
scale_x_continuous(breaks = seq(0,15,2)) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme(plot.title = element_text(size = 13),
text = element_text(size = 13),
axis.text = element_text(size = 13),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")) +
xlab("Peak antibody level (log2 dilution) \n posterior individual means \n (Pomona)") +
ylab("Peak antibody level (log2 dilution) \n posterior individual means \n (Autumnalis)") +
labs(tag = "(A)")
cor.plot.individual
joint.dens = chains.burn.df[,c("peak.titer.overall.pomona","peak.titer.overall.aut")]
joint.dens$dens = get_density(chains.burn.df$peak.titer.overall.pomona,chains.burn.df$peak.titer.overall.aut,n = 80)
joint.plot.peak.correlation = ggplot(joint.dens,aes(x = peak.titer.overall.pomona, y = peak.titer.overall.aut,color=dens)) +
geom_point(alpha = 0.4,size = 0.5) +
scale_colour_gradientn(colors = rev(brewer.pal(n = 6,"Spectral")),name="Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text = element_text(size = 13),
axis.text = element_text(size = 13),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")) +
xlab("Peak antibody level (log2 dilution) \n posterior overall mean \n (Pomona)") +
ylab("Peak antibody level (log2 dilution) \n posterior overall mean \n (Autumnalis)") +
labs(tag = "(B)")
joint.plot.peak.correlation
Combined plot peak titer correlations for manuscript
comb.plot.correlation = cor.plot.individual + joint.plot.peak.correlation
comb.plot.correlation
3.1.2 Decay rate
Nsamp = 200
means.pomona = sample(chains.burn.df$decay.rate.overall.pomona,Nsamp,replace = F)
sds.pomona = sample(chains.burn.df$decay.rate.sd.overall.pomona,Nsamp,replace = F)
means.aut = sample(chains.burn.df$decay.rate.overall.aut,Nsamp,replace = F)
sds.aut = sample(chains.burn.df$decay.rate.sd.overall.aut,Nsamp,replace = F)
ggplot(data = data.frame(x = seq(0,0.0016,0.0001)),aes(x=x)) +
#scale_x_continuous(breaks=0:15) +
mapply(function(mean,sd){
stat_function(fun = dnorm, args = list(mean = mean,sd = sd),aes(color="Autumnalis"),size=0.4)
},
mean = means.aut,
sd = sds.aut
) +
stat_function(fun = dnorm, args = list(mean = decay.rate.overall.mean.aut,sd = decay.rate.sd.overall.mean.aut),color = brewer.pal(11,"Spectral")[1],size=1.5) +
geom_vline(xintercept=decay.rate.overall.mean.aut,col=brewer.pal(11,"Spectral")[1],size=0.8,alpha=0.6) +
mapply(function(mean,sd){
stat_function(fun = dnorm,args = list(mean = mean,sd = sd),aes(color="Pomona"),size=0.4)
},
mean = means.pomona,
sd = sds.pomona
) +
stat_function(fun = dnorm, args = list(mean = decay.rate.overall.mean.pomona,sd = decay.rate.sd.overall.mean.pomona),color = brewer.pal(11,"Spectral")[10],size=1.5) +
geom_vline(xintercept=decay.rate.overall.mean.pomona,col=brewer.pal(11,"Spectral")[10],size=0.8,alpha=0.6) +
scale_color_manual("Serovar",values = alpha(c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1]),0.3)) +
xlab("Decay rate (1/day)") +
ylab("Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = c(0.85,0.75),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent")
)
plot2 = ggplot(data = data.frame(x = seq(0,0.0017,0.0001)),aes(x=x)) +
#scale_x_continuous(breaks=0:15) +
mapply(function(mean,sd){
stat_function(fun = dnorm, args = list(mean = mean,sd = sd),aes(color="Autumnalis"),size=0.2,alpha=0.1)
},
mean = means.aut,
sd = sds.aut
) +
#geom_vline(xintercept=decay.rate.overall.mean.aut,col=brewer.pal(11,"Spectral")[1],size=0.8,alpha=0.6) +
mapply(function(mean,sd){
stat_function(fun = dnorm,args = list(mean = mean,sd = sd),aes(color="Pomona"),size=0.2,alpha=0.1)
},
mean = means.pomona,
sd = sds.pomona
) +
stat_function(fun = dnorm, args = list(mean = decay.rate.overall.mean.aut,sd = decay.rate.sd.overall.mean.aut),color = brewer.pal(11,"Spectral")[1],size=1.5) +
stat_function(fun = dnorm, args = list(mean = decay.rate.overall.mean.pomona,sd = decay.rate.sd.overall.mean.pomona),color = brewer.pal(11,"Spectral")[10],size=1.5) +
#geom_vline(xintercept=decay.rate.overall.mean.pomona,col=brewer.pal(11,"Spectral")[10],size=0.8,alpha=0.6) +
scale_color_manual("Serovar",values = alpha(c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1]),0.3)) +
scale_x_continuous(breaks = c(0,0.00075,0.0015),labels = c("0","0.00075","0.0015")) +
xlab("Decay rate \n(1/day)") +
ylab("Density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.title = element_text(size = 12),
legend.text = element_text(size = 11),
#legend.position = "none",
legend.position = c(0.75,0.85),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
) +
labs(tag = "(B)")3.1.3 Fitted function
ggplot() +
#geom_line(data=fit.dat.cloud.aut, aes(x = time, y = titer, group = samp), col = brewer.pal(11,"Spectral")[1],alpha=0.5,size=0.3) +
geom_line(data=fit.dat.aut,aes(x = time, y = titer,col="Autumnalis"),size=1.5) +
#geom_line(data=fit.dat.cloud.pomona, aes(x = time, y = titer, group = samp), col = brewer.pal(11,"Spectral")[10],alpha=0.5,size=0.3) +
geom_line(data=fit.dat.pomona,aes(x = time, y = titer,col="Pomona"),size=1.5) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (log2 dilution)")
ggplot() +
geom_line(data=fit.dat.cloud.aut, aes(x = time, y = titer, group = samp, col = "Autumnalis"),alpha=0.5,size=0.3) +
#geom_line(data=fit.dat.aut,aes(x = time, y = titer,col="Autumnalis"),size=1.5) +
geom_line(data=fit.dat.cloud.pomona, aes(x = time, y = titer, group = samp, col = "Pomona"),alpha=0.5,size=0.3) +
#geom_line(data=fit.dat.pomona,aes(x = time, y = titer,col="Pomona"),size=1.5) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (log2 dilution)")
plot3 = ggplot() +
geom_line(data=fit.dat.cloud.aut, aes(x = time, y = titer, group = samp, col = "Autumnalis"),alpha=0.1,size=0.2) +
geom_line(data=fit.dat.cloud.pomona, aes(x = time, y = titer, group = samp, col = "Pomona"),alpha=0.1,size=0.2) +
geom_line(data=fit.dat.aut,aes(x = time, y = titer,col="Autumnalis"),size=1.5) +
geom_line(data=fit.dat.pomona,aes(x = time, y = titer,col="Pomona"),size=1.5) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
scale_y_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 12),
text = element_text(size = 11),
axis.text = element_text(size = 11),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)),
legend.position = c(0.85,0.75),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"))+
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (log2 dilution)")
labs(tag = "(C)")## $tag
## [1] "(C)"
##
## attr(,"class")
## [1] "labels"3.2 Combined figures peak antibody level, decay rate, fitted functions
plot1 + plot2 + plot3 +
plot_layout(widths = c(1,1,2))
plot1 + plot2
joint.plot.peak.decay
comb.plot = plot1 + plot2 + joint.plot.peak.decay +
plot_layout(widths = c(4,4,4))
comb.plot
3.3 Combined figure function comparison and fitted Pom and Aut
# import posterior estimates for the three functions
all.fun.post.overall = readRDS("Function_comparison_post_overall.RDS")
fit.dat.single = linear.fun(peak.titer = all.fun.post.overall$peak.titer.mean[1], slope = -all.fun.post.overall$decay.mean[1], time = 0:4000)
fit.dat.single = data.frame(titer = fit.dat.single, time = 0:4000)
fit.dat.single$model = "Single exp"
fit.dat.single = fit.dat.single[-which(fit.dat.single$titer<0),]
fit.dat.double = exp.fun(start.titer = all.fun.post.overall$peak.titer.mean[2], rate = -all.fun.post.overall$decay.mean[2], time = 0:4000)
fit.dat.double = data.frame(titer = fit.dat.double, time = 0:4000)
fit.dat.double$model = "Double exp"
fit.dat.double.aut = exp.fun(start.titer = all.fun.post.overall$peak.titer.aut.mean[2], rate = -all.fun.post.overall$decay.aut.mean[2], time = 0:4000)
fit.dat.double.aut = data.frame(titer = fit.dat.double.aut, time = 0:4000)
fit.dat.double.aut$model = "Double exp (Autumnalis)"
fit.dat.power = power.fun(peak.titer = all.fun.post.overall$peak.titer.mean[3], shape = all.fun.post.overall$shape.mean[3],scale = all.fun.post.overall$scale.mean[3], time = 0:4000)
fit.dat.power = data.frame(titer = fit.dat.power, time = 0:4000)
fit.dat.power$model = "Power"
fit.dat = rbind(fit.dat.single,fit.dat.double,fit.dat.power)
fit.dat$model = factor(fit.dat$model,levels = c("Single exp","Double exp","Power"))
plot.1.functions = ggplot(data=fit.dat,aes(x = time, y = titer,color=model,group=model)) +
geom_line(size=1.2) +
scale_color_manual("Model",values = brewer.pal(4,"Spectral"),labels = c("Single exponential","Double exponential","Power")) +
scale_y_continuous(limits = c(0,8),breaks = 0:8) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = c(0.65,0.75),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(colour = "transparent", fill = "transparent")
) +
xlab("Time since peak \nantibody level (days)") +
ylab("Antibody level (log2 dilution)") +
labs(tag="(A)")
plot2.with.aut = ggplot() +
#geom_line(data = observed.data.for.fitting,aes(x = time.since.peak,y = titer.pomona,col = id,group = id),alpha = 0.2,size = 0.2,color=brewer.pal(11,"Spectral")[10]) +
geom_point(data = observed.data.for.fitting,aes(x = time.since.peak,y = titer.pomona-0.1,col = id,group = id),size=1,alpha = 0.4,color=brewer.pal(11,"Spectral")[10]) +
#geom_line(data = observed.data.for.fitting,aes(x = time.since.peak,y = titer.aut,col = id,group = id),alpha = 0.2,size = 0.2,color=brewer.pal(11,"Spectral")[1]) +
geom_point(data = observed.data.for.fitting,aes(x = time.since.peak,y = titer.aut+0.1,col = id,group = id),size=1,alpha = 0.4,color=brewer.pal(11,"Spectral")[1]) +
geom_line(data=fit.dat.double,aes(x = time, y = titer,col="Pomona"),size=1.6) +
geom_line(data=fit.dat.double.aut,aes(x = time, y = titer,col="Autumnalis"),size=1.6) +
#scale_color_gradientn(colours = brewer.pal(4,"Spectral")) +
scale_color_manual("Serovar",values = alpha(c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1]),1)) +
scale_y_continuous(limits = c(0,12),breaks = 0:12) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = c(0.7,0.8),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(colour = "transparent", fill = "transparent")
) +
#ggtitle("Double exponential") +
xlab("Time since peak \nantibody level (days)") +
ylab("Antibody level (log2 dilution)") +
labs(tag = "(B)")
plotA = plot.1.functions + plot2.with.aut
plotA
# dataframe to store estimated parameters
model.outputs = data.frame(id = 1:N.inds,
pittag = unique(observed.data.for.fitting$Pittag),
toi.mid.interval = round(neg.intervals[1:N.inds]/2),
toi.estimated.mean = NA,
toi.estimated.median = NA,
toi.estimated.maxdens = NA,
toi.estimated.mean.95ci.low = NA,
toi.estimated.mean.95ci.high = NA,
toi.estimated.maxdens.95hdi.low = NA,
toi.estimated.maxdens.95hdi.high = NA,
toi.information.gained.95cri.perc = NA,
toi.information.gained.80cri.perc = NA,
toi.information.gained.50cri.perc = NA,
kl.divergence = NA,
peak.titer.estimated.mean.pomona = NA,
peak.titer.estimated.median.pomona = NA,
peak.titer.estimated.maxdens.pomona = NA,
peak.titer.estimated.mean.95ci.low.pomona = NA,
peak.titer.estimated.mean.95ci.high.pomona = NA,
peak.titer.estimated.maxdens.95hdi.low.pomona = NA,
peak.titer.estimated.maxdens.95hdi.high.pomona = NA,
decay.rate.estimated.mean.pomona = NA,
decay.rate.estimated.median.pomona = NA,
decay.rate.estimated.maxdens.pomona = NA,
decay.rate.estimated.mean.95ci.low.pomona = NA,
decay.rate.estimated.mean.95ci.high.pomona = NA,
decay.rate.estimated.maxdens.95hdi.low.pomona = NA,
decay.rate.estimated.maxdens.95hdi.high.pomona = NA,
peak.titer.estimated.mean.aut = NA,
peak.titer.estimated.median.aut = NA,
peak.titer.estimated.maxdens.aut = NA,
peak.titer.estimated.mean.95ci.low.aut = NA,
peak.titer.estimated.mean.95ci.high.aut = NA,
peak.titer.estimated.maxdens.95hdi.low.aut = NA,
peak.titer.estimated.maxdens.95hdi.high.aut = NA,
decay.rate.estimated.mean.aut = NA,
decay.rate.estimated.median.aut = NA,
decay.rate.estimated.maxdens.aut = NA,
decay.rate.estimated.mean.95ci.low.aut = NA,
decay.rate.estimated.mean.95ci.high.aut = NA,
decay.rate.estimated.maxdens.95hdi.low.aut = NA,
decay.rate.estimated.maxdens.95hdi.high.aut = NA
)
# matrix for plotting individual posteriors of time of infection
# nrow = number of iterations to store
theta.all = matrix(data = NA, ncol = N.inds, nrow = 6000)
# print figures?
plot.figs = F
teller = 1
for(i in 1:N.inds){
chains.current.individual.df = chains.burn.df[,c(paste0("peak.titer.pomona.",i),paste0("peak.titer.aut.",i),paste0("decay.rate.pomona.",i),paste0("decay.rate.aut.",i),paste0("toi.",i),"iteration","chain")]
colnames(chains.current.individual.df) = c("peak.titer.pomona","peak.titer.aut","decay.rate.pomona","decay.rate.aut","toi","iteration","chain")
theta.all[,i] = chains.current.individual.df[which(chains.current.individual.df$iteration %in% (max(chains.current.individual.df$iteration)-999):max(chains.current.individual.df$iteration)),"toi"]
model.outputs[teller,"toi.estimated.mean"] = round(mean(chains.current.individual.df[,"toi"]))
model.outputs[teller,"peak.titer.estimated.mean.pomona"] = round(mean(chains.current.individual.df[,"peak.titer.pomona"]),1)
model.outputs[teller,"decay.rate.estimated.mean.pomona"] = round(mean(chains.current.individual.df[,"decay.rate.pomona"]),6)
model.outputs[teller,"peak.titer.estimated.mean.aut"] = round(mean(chains.current.individual.df[,"peak.titer.aut"]),1)
model.outputs[teller,"decay.rate.estimated.mean.aut"] = round(mean(chains.current.individual.df[,"decay.rate.aut"]),6)
model.outputs[teller,"toi.estimated.median"] = median(round(chains.current.individual.df[,"toi"]))
model.outputs[teller,"peak.titer.estimated.median.pomona"] = median(round(chains.current.individual.df[,"peak.titer.pomona"],1))
model.outputs[teller,"decay.rate.estimated.median.pomona"] = median(round(chains.current.individual.df[,"decay.rate.pomona"],6))
model.outputs[teller,"peak.titer.estimated.median.aut"] = median(round(chains.current.individual.df[,"peak.titer.aut"],1))
model.outputs[teller,"decay.rate.estimated.median.aut"] = median(round(chains.current.individual.df[,"decay.rate.aut"],6))
dens = density(chains.current.individual.df[,"toi"],bw = 5,from = neg.intervals[i],to=0)
model.outputs[teller,"toi.estimated.maxdens"] = round(dens$x[which.max(dens$y)])
hpd.int.toi = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F) # highest posterior density credible intervals
# quantile(chains.current.individual.df[which(chains.current.individual.df$iteration > burn.in),2],c(0.025,0.975))
model.outputs[teller,"toi.estimated.maxdens.95hdi.low"] = round(hpd.int.toi[1])
model.outputs[teller,"toi.estimated.maxdens.95hdi.high"] = round(hpd.int.toi[2])
model.outputs[teller,"toi.information.gained.95cri.perc"] = 1-abs((as.numeric(hpd.int.toi[2]-hpd.int.toi[1])/(0.95*neg.intervals[i])))
hpd.int.toi.80 = HDInterval::hdi(dens,credMass = 0.80,allowSplit=F) # highest posterior density credible intervals
model.outputs[teller,"toi.information.gained.80cri.perc"] = 1-abs((as.numeric(hpd.int.toi.80[2]-hpd.int.toi.80[1])/(0.8*neg.intervals[i])))
hpd.int.toi.50 = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F) # highest posterior density credible intervals
model.outputs[teller,"toi.information.gained.50cri.perc"] = 1-abs((as.numeric(hpd.int.toi.50[2]-hpd.int.toi.50[1])/(0.5*neg.intervals[i])))
### Relative entropy / Kullback-Leibler divergence
prior.values = neg.intervals[i]:0
posterior.values = chains.current.individual.df[,"toi"]
model.outputs[teller,"kl.divergence"] = kl.divergence(prior.values,posterior.values)
mean.eti.toi = mean.eti(chains.current.individual.df[,"toi"])
model.outputs[teller,"toi.estimated.mean.95ci.low"] = round(mean.eti.toi[1])
model.outputs[teller,"toi.estimated.mean.95ci.high"] = round(mean.eti.toi[2])
dens = density(chains.current.individual.df[,"peak.titer.pomona"])
model.outputs[teller,"peak.titer.estimated.maxdens.pomona"] = round(dens$x[which.max(dens$y)],1)
hpd.int.peak.titer.pomona = HDInterval::hdi(dens,allowSplit=F)
mean.eti.peak.titer.pomona = mean.eti(chains.current.individual.df[,"peak.titer.pomona"])
model.outputs[teller,"peak.titer.estimated.maxdens.95hdi.low.pomona"] = round(hpd.int.peak.titer.pomona[1],1)
model.outputs[teller,"peak.titer.estimated.maxdens.95hdi.high.pomona"] = round(hpd.int.peak.titer.pomona[2],1)
model.outputs[teller,"peak.titer.estimated.mean.95ci.low.pomona"] = round(mean.eti.peak.titer.pomona[1],1)
model.outputs[teller,"peak.titer.estimated.mean.95ci.high.pomona"] = round(mean.eti.peak.titer.pomona[2],1)
dens = density(chains.current.individual.df[,"peak.titer.aut"])
model.outputs[teller,"peak.titer.estimated.maxdens.aut"] = round(dens$x[which.max(dens$y)],1)
hpd.int.peak.titer.aut = HDInterval::hdi(dens,allowSplit=F)
mean.eti.peak.titer.aut = mean.eti(chains.current.individual.df[,"peak.titer.aut"])
model.outputs[teller,"peak.titer.estimated.maxdens.95hdi.low.aut"] = round(hpd.int.peak.titer.aut[1],1)
model.outputs[teller,"peak.titer.estimated.maxdens.95hdi.high.aut"] = round(hpd.int.peak.titer.aut[2],1)
model.outputs[teller,"peak.titer.estimated.mean.95ci.low.aut"] = round(mean.eti.peak.titer.aut[1],1)
model.outputs[teller,"peak.titer.estimated.mean.95ci.high.aut"] = round(mean.eti.peak.titer.aut[2],1)
dens = density(chains.current.individual.df[,"decay.rate.pomona"])
model.outputs[teller,"decay.rate.estimated.maxdens.pomona"] = round(dens$x[which.max(dens$y)],6)
hpd.int.decay.rate.pomona = HDInterval::hdi(dens,allowSplit=F)
mean.eti.decay.rate.pomona = mean.eti(chains.current.individual.df[,"decay.rate.pomona"])
model.outputs[teller,"decay.rate.estimated.maxdens.95hdi.low.pomona"] = round(hpd.int.decay.rate.pomona[1],6)
model.outputs[teller,"decay.rate.estimated.maxdens.95hdi.high.pomona"] = round(hpd.int.decay.rate.pomona[2],6)
model.outputs[teller,"decay.rate.estimated.mean.95ci.low.pomona"] = round(mean.eti.decay.rate.pomona[1],6)
model.outputs[teller,"decay.rate.estimated.mean.95ci.high.pomona"] = round(mean.eti.decay.rate.pomona[2],6)
dens = density(chains.current.individual.df[,"decay.rate.aut"])
model.outputs[teller,"decay.rate.estimated.maxdens.aut"] = round(dens$x[which.max(dens$y)],6)
hpd.int.decay.rate.aut = HDInterval::hdi(dens,allowSplit=F)
mean.eti.decay.rate.aut = mean.eti(chains.current.individual.df[,"decay.rate.aut"])
model.outputs[teller,"decay.rate.estimated.maxdens.95hdi.low.aut"] = round(hpd.int.decay.rate.aut[1],6)
model.outputs[teller,"decay.rate.estimated.maxdens.95hdi.high.aut"] = round(hpd.int.decay.rate.aut[2],6)
model.outputs[teller,"decay.rate.estimated.mean.95ci.low.aut"] = round(mean.eti.decay.rate.aut[1],6)
model.outputs[teller,"decay.rate.estimated.mean.95ci.high.aut"] = round(mean.eti.decay.rate.aut[2],6)
plot1 = ggplot(data=chains.current.individual.df,aes(x=toi)) +
geom_density(color=brewer.pal(11,"Spectral")[10],fill=brewer.pal(11,"Spectral")[10],alpha=0.6,size=1) +
geom_vline(xintercept = model.outputs[teller,"toi.estimated.maxdens"],col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept = model.outputs[teller,"toi.estimated.maxdens.95hdi.low"],col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept = model.outputs[teller,"toi.estimated.maxdens.95hdi.high"],col="black",size=0.8,linetype="dotted",alpha=0.6) +
theme_light(base_family = "Avenir Next") +
ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("\u03B8 (days)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = "none"
)
#plot2 = plot.MCMC.chains(input.data = chains.current.individual.df, column.to.plot = which(colnames(chains.current.individual.df)=="toi"), y.axis.label = "Theta",thinning = 20, title = paste0("Individual ",model.outputs[i,"pittag"]))
if(plot.figs==T) print(plot1)
plot1 = ggplot(data = data.frame(x = 0:15),aes(x=x)) +
stat_function(fun = dnorm, args = list(mean = model.outputs[teller,"peak.titer.estimated.maxdens.pomona"],sd = sd(chains.current.individual.df$peak.titer.pomona)),aes(col="Pomona"),size=1.5) +
geom_vline(xintercept = model.outputs[teller,"peak.titer.estimated.maxdens.pomona"],col=brewer.pal(11,"Spectral")[10]) +
stat_function(fun = dnorm, args = list(mean = model.outputs[teller,"peak.titer.estimated.maxdens.aut"],sd = sd(chains.current.individual.df$peak.titer.aut)),aes(col="Autumnalis"),size=1.5) +
geom_vline(xintercept = model.outputs[teller,"peak.titer.estimated.maxdens.aut"],col=brewer.pal(11,"Spectral")[1]) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
theme_light(base_family = "Avenir Next") +
ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("Peak antibody level (log2 dilution)") +
ylab("Posterior density") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
)
plot2 = ggplot(data = data.frame(x = seq(-0.00015,0.002,0.0001)),aes(x=x)) +
stat_function(fun = dnorm, args = list(mean = model.outputs[teller,"decay.rate.estimated.maxdens.pomona"],sd = sd(chains.current.individual.df$decay.rate.pomona)),aes(col="Pomona"),size=1.5) +
geom_vline(xintercept = model.outputs[teller,"decay.rate.estimated.maxdens.pomona"],col=brewer.pal(11,"Spectral")[10]) +
stat_function(fun = dnorm, args = list(mean = model.outputs[teller,"decay.rate.estimated.maxdens.aut"],sd = sd(chains.current.individual.df$decay.rate.aut)),aes(col="Autumnalis"),size=1.5) +
geom_vline(xintercept = model.outputs[teller,"decay.rate.estimated.maxdens.aut"],col=brewer.pal(11,"Spectral")[1]) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("Decay rate (1/day)") +
ylab("Posterior density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
)
if(plot.figs==T) print(plot1)
if(plot.figs==T) print(plot2)
# pomona
# sample N values from posterior distribution to get sense of error
N.samp = 200
set.seed(1234);peak.titer.samp = sample(chains.current.individual.df$peak.titer.pomona[which(chains.current.individual.df$peak.titer.pomona > hpd.int.peak.titer.pomona[1] & chains.current.individual.df$peak.titer.pomona < hpd.int.peak.titer.pomona[2])], size = N.samp, replace = T)
set.seed(1234);decay.rate.samp = sample(chains.current.individual.df$decay.rate.pomona[which(chains.current.individual.df$decay.rate.pomona > hpd.int.decay.rate.pomona[1] & chains.current.individual.df$decay.rate.pomona < hpd.int.decay.rate.pomona[2])], size = N.samp, replace = T)
plot.times = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)])
fit.dat.cloud.pomona = data.frame(time = rep(plot.times,N.samp), samp = rep(1:N.samp,each = length(plot.times)), titer = NA)
for(ii in 1:N.samp){
fit.dat.cloud.pomona$titer[which(fit.dat.cloud.pomona$samp == ii)] = exp.fun(start.titer = peak.titer.samp[ii], rate = -decay.rate.samp[ii], time = plot.times)
}
fit.dat.pomona = exp.fun(start.titer = model.outputs[teller,"peak.titer.estimated.mean.pomona"], rate = -model.outputs[teller,"decay.rate.estimated.mean.pomona"], time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]))
fit.dat.pomona = data.frame(titer = fit.dat.pomona, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]))
plot3 = ggplot() +
#geom_line(data = observed.data.for.fitting[which(observed.data.for.fitting$id==i),],aes(x = time.since.peak,y = titer),alpha = 0.8,size = 1.5,color="#F46D43") +
geom_line(data=fit.dat.cloud.pomona, aes(x = time, y = titer, group = samp), col = brewer.pal(11,"Spectral")[10],alpha=0.2,size=0.4) +
geom_line(data=fit.dat.pomona,aes(x = time, y = titer),col=brewer.pal(11,"Spectral")[10],size=1.5,alpha=1) +
geom_point(data = observed.data.for.fitting[which(observed.data.for.fitting$id==i),],aes(x = time.since.peak,y = titer.pomona),size=3,alpha = 1,color="black") +
#scale_color_gradientn(colours = brewer.pal(11,"Spectral")) +
scale_y_continuous(limits = c(0,13),breaks=0:13) +
ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (Pomona) (log2 dilution)")
if(plot.figs==T) print(plot3)
# aut
# sample N values from posterior distribution to get sense of error
N.samp = 200
set.seed(1234);peak.titer.samp = sample(chains.current.individual.df$peak.titer.aut[which(chains.current.individual.df$peak.titer.aut > hpd.int.peak.titer.aut[1] & chains.current.individual.df$peak.titer.aut < hpd.int.peak.titer.aut[2])], size = N.samp, replace = T)
set.seed(1234);decay.rate.samp = sample(chains.current.individual.df$decay.rate.aut[which(chains.current.individual.df$decay.rate.aut > hpd.int.decay.rate.aut[1] & chains.current.individual.df$decay.rate.aut < hpd.int.decay.rate.aut[2])], size = N.samp, replace = T)
plot.times = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)])
fit.dat.cloud.aut = data.frame(time = rep(plot.times,N.samp), samp = rep(1:N.samp,each = length(plot.times)), titer = NA)
for(ii in 1:N.samp){
fit.dat.cloud.aut$titer[which(fit.dat.cloud.aut$samp == ii)] = exp.fun(start.titer = peak.titer.samp[ii], rate = -decay.rate.samp[ii], time = plot.times)
}
if(is.finite(observed.data.for.fitting$titer.aut[which(observed.data.for.fitting$id==i)][1])){
fit.dat.aut = exp.fun(start.titer = model.outputs[teller,"peak.titer.estimated.mean.aut"], rate = -model.outputs[teller,"decay.rate.estimated.mean.aut"], time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]))
fit.dat.aut = data.frame(titer = fit.dat.aut, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]))
plot4 = ggplot() +
#geom_line(data = observed.data.for.fitting[which(observed.data.for.fitting$id==i),],aes(x = time.since.peak,y = titer),alpha = 0.8,size = 1.5,color="#F46D43") +
geom_line(data=fit.dat.cloud.aut, aes(x = time, y = titer, group = samp), col = brewer.pal(11,"Spectral")[1],alpha=0.2,size=0.4) +
geom_line(data=fit.dat.aut,aes(x = time, y = titer),col=brewer.pal(11,"Spectral")[1],size=1.5,alpha=1) +
geom_point(data = observed.data.for.fitting[which(observed.data.for.fitting$id==i),],aes(x = time.since.peak,y = titer.aut),size=3,alpha = 1,color="black") +
#scale_color_gradientn(colours = brewer.pal(11,"Spectral")) +
scale_y_continuous(limits = c(0,13),breaks=0:13) +
ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
)+
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (Autumnalis) (log2 dilution)")
if(plot.figs==T) print(plot4)
}
# print(paste0("% seroconversion interval reduction: ",round(model.outputs$toi.information.gained.95cri.perc[teller],4)))
# print(paste0("KL-divergence: ",round(model.outputs$kl.divergence[teller],4)))
# print(paste0("Individual ",i," of ",N.inds))
# print("----------------------------------------------")
# print("----------------------------------------------")
# print("----------------------------------------------")
teller = teller + 1
}
3.4 Individual posteriors and chains
Posterior densities are shown after removing burn-in
iterations.
Solid red line = estimated posterior maximum density
Weaker red line = estimated posterior mean
Figure for main text:
# model.outputs$pittag[which(model.outputs$toi.information.gained.95cri.perc %in% head(sort(model.outputs$toi.information.gained.95cri.perc)))]
# model.outputs$pittag[which(model.outputs$toi.information.gained.95cri.perc %in% tail(sort(model.outputs$toi.information.gained.95cri.perc)))]
# observed.data.for.fitting[which(observed.data.for.fitting$Pittag=="B6069"),]
pittag.to.plot = c("12672","B6069")
# individual 1
i = observed.data.for.fitting$id[which(observed.data.for.fitting$Pittag==pittag.to.plot[1])][1]
chains.current.individual.df = chains.burn.df[,c(paste0("peak.titer.pomona.",i),paste0("peak.titer.aut.",i),paste0("decay.rate.pomona.",i),paste0("decay.rate.aut.",i),paste0("toi.",i),"iteration","chain")]
colnames(chains.current.individual.df) = c("peak.titer.pomona","peak.titer.aut","decay.rate.pomona","decay.rate.aut","toi","iteration","chain")
cur.peak.titer.pomona = round(mean(chains.current.individual.df[,"peak.titer.pomona"]),1)
dens = density(chains.current.individual.df[,"peak.titer.pomona"])
hpd.int.peak.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.peak.pomona.low = round(hpd.int.peak.pomona[1])
cur.95.peak.pomona.high = round(hpd.int.peak.pomona[2])
hpd.int.peak.pomona = HDInterval::hdi(dens,credMass = 0.5,allowSplit=F)
cur.50.peak.pomona.low = round(hpd.int.peak.pomona[1])
cur.50.peak.pomona.high = round(hpd.int.peak.pomona[2])
cur.decay.pomona = round(mean(chains.current.individual.df[,"decay.rate.pomona"]),6)
dens = density(chains.current.individual.df[,"decay.rate.pomona"])
hpd.int.decay.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.decay.pomona.low = hpd.int.decay.pomona[1]
cur.95.decay.pomona.high = hpd.int.decay.pomona[2]
hpd.int.decay.pomona = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F)
cur.50.decay.pomona.low = hpd.int.decay.pomona[1]
cur.50.decay.pomona.high = hpd.int.decay.pomona[2]
cur.peak.titer.aut = round(mean(chains.current.individual.df[,"peak.titer.aut"]),1)
dens = density(chains.current.individual.df[,"peak.titer.aut"])
hpd.int.peak.aut = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.peak.aut.low = round(hpd.int.peak.aut[1])
cur.95.peak.aut.high = round(hpd.int.peak.aut[2])
hpd.int.peak.aut = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F)
cur.50.peak.aut.low = round(hpd.int.peak.aut[1])
cur.50.peak.aut.high = round(hpd.int.peak.aut[2])
cur.decay.aut = round(mean(chains.current.individual.df[,"decay.rate.aut"]),6)
dens = density(chains.current.individual.df[,"decay.rate.aut"])
hpd.int.decay.aut = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.decay.aut.low = hpd.int.decay.aut[1]
cur.95.decay.aut.high = hpd.int.decay.aut[2]
hpd.int.decay.aut = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F)
cur.50.decay.aut.low = hpd.int.decay.aut[1]
cur.50.decay.aut.high = hpd.int.decay.aut[2]
dens = density(chains.current.individual.df[,"toi"],bw = 5,from = neg.intervals[i],to=0)
cur.toi = round(dens$x[which.max(dens$y)])
hpd.int.toi = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.low = round(hpd.int.toi[1])
cur.95.high = round(hpd.int.toi[2])
hpd.int.toi = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F)
cur.50.low = round(hpd.int.toi[1])
cur.50.high = round(hpd.int.toi[2])
plot1a = ggplot(data=chains.current.individual.df,aes(x=toi)) +
geom_density(color=brewer.pal(4,"Spectral")[2],fill=brewer.pal(4,"Spectral")[2],alpha=0.6,size=1) +
geom_vline(xintercept = cur.toi,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept = cur.95.low,col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept = cur.95.high,col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept = cur.50.low,col="black",size=0.8,linetype=2,alpha=0.6) +
geom_vline(xintercept = cur.50.high,col="black",size=0.8,linetype=2,alpha=0.6) +
theme_light(base_family = "Avenir Next") +
#ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("\u03B8 (days)") +
ylab("Posterior density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
#legend.position = c(0.78,0.85),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
)+
labs(tag = "(A)")
plot1b = ggplot(data = data.frame(x = 0:15),aes(x=x)) +
stat_function(fun = dnorm, args = list(mean = cur.peak.titer.pomona,sd = sd(chains.current.individual.df$peak.titer.pomona)),aes(col="Pomona"),size=1.5) +
#geom_vline(xintercept = cur.peak.titer.pomona,col=brewer.pal(11,"Spectral")[10]) +
stat_function(fun = dnorm, args = list(mean = cur.peak.titer.aut,sd = sd(chains.current.individual.df$peak.titer.aut)),aes(col="Autumnalis"),size=1.5) +
#geom_vline(xintercept = cur.peak.titer.aut,col=brewer.pal(11,"Spectral")[1]) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
theme_light(base_family = "Avenir Next") +
#ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("Peak antibody level (log2 dilution)") +
ylab("Posterior density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = c(0.2,0.8),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
)
plot1c = ggplot(data = data.frame(x = seq(-0.00015,0.002,0.0001)),aes(x=x)) +
stat_function(fun = dnorm, args = list(mean = cur.decay.pomona,sd = sd(chains.current.individual.df$decay.rate.pomona)),aes(col="Pomona"),size=1.5) +
#geom_vline(xintercept = cur.decay.pomona,col=brewer.pal(11,"Spectral")[10]) +
stat_function(fun = dnorm, args = list(mean = cur.decay.aut,sd = sd(chains.current.individual.df$decay.rate.aut)),aes(col="Autumnalis"),size=1.5) +
#geom_vline(xintercept = cur.peak.titer.aut,col=brewer.pal(11,"Spectral")[1]) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
theme_light(base_family = "Avenir Next") +
#ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("Decay rate (1/day)") +
ylab("Posterior density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = c(0.15,0.8),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
)
# pomona
# sample N values from posterior distribution to get sense of error
N.samp = 200
set.seed(1234);peak.titer.samp.pomona = sample(chains.current.individual.df$peak.titer.pomona[which(chains.current.individual.df$peak.titer.pomona > cur.95.peak.pomona.low & chains.current.individual.df$peak.titer.pomona < cur.95.peak.pomona.high)], size = N.samp, replace = T)
set.seed(1234);decay.rate.samp.pomona = sample(chains.current.individual.df$decay.rate.pomona[which(chains.current.individual.df$decay.rate.pomona > cur.95.decay.pomona.low & chains.current.individual.df$decay.rate.pomona < cur.95.decay.pomona.high)], size = N.samp, replace = T)
set.seed(1234);peak.titer.samp.aut = sample(chains.current.individual.df$peak.titer.aut[which(chains.current.individual.df$peak.titer.aut > cur.95.peak.aut.low & chains.current.individual.df$peak.titer.aut < cur.95.peak.aut.high)], size = N.samp, replace = T)
set.seed(1234);decay.rate.samp.aut = sample(chains.current.individual.df$decay.rate.aut[which(chains.current.individual.df$decay.rate.aut > cur.95.decay.aut.low & chains.current.individual.df$decay.rate.aut < cur.95.decay.aut.high)], size = N.samp, replace = T)
plot.times = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)])
fit.dat.cloud.pomona = data.frame(time = rep(plot.times,N.samp), samp = rep(1:N.samp,each = length(plot.times)), titer = NA,Serovar = "Pomona")
fit.dat.cloud.aut = data.frame(time = rep(plot.times,N.samp), samp = rep(1:N.samp,each = length(plot.times)), titer = NA,Serovar = "Autumnalis")
for(ii in 1:N.samp){
fit.dat.cloud.pomona$titer[which(fit.dat.cloud.pomona$samp == ii)] = exp.fun(start.titer = peak.titer.samp.pomona[ii], rate = -decay.rate.samp.pomona[ii], time = plot.times)
fit.dat.cloud.aut$titer[which(fit.dat.cloud.aut$samp == ii)] = exp.fun(start.titer = peak.titer.samp.aut[ii], rate = -decay.rate.samp.aut[ii], time = plot.times)
}
fit.dat.cloud.both = rbind(fit.dat.cloud.pomona,fit.dat.cloud.aut)
fit.dat.cloud.both$samp = paste(fit.dat.cloud.both$Serovar,fit.dat.cloud.both$samp)
fit.dat.cloud.both$Serovar = factor(fit.dat.cloud.both$Serovar,levels = c("Autumnalis","Pomona"))
fit.dat.pomona = exp.fun(start.titer = cur.peak.titer.pomona, rate = -cur.decay.pomona, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]))
fit.dat.aut = exp.fun(start.titer = cur.peak.titer.aut, rate = -cur.decay.aut, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]))
fit.dat.pomona = data.frame(titer = fit.dat.pomona, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]),Serovar = "Pomona")
fit.dat.aut = data.frame(titer = fit.dat.aut, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]),Serovar = "Autumnalis")
fit.dat.both = rbind(fit.dat.pomona,fit.dat.aut)
cur.observed.data.for.fitting = gather(observed.data.for.fitting,Serovar,titer,c("titer.pomona","titer.aut"))
cur.observed.data.for.fitting$Serovar[which(cur.observed.data.for.fitting$Serovar=="titer.pomona")] = "Pomona"
cur.observed.data.for.fitting$Serovar[which(cur.observed.data.for.fitting$Serovar=="titer.aut")] = "Autumnalis"
# cur.observed.data.for.fitting$Serovar = factor(cur.observed.data.for.fitting$Serovar,levels = c("Pomona","Autumnalis"))
plot1d = ggplot() +
#geom_line(data = observed.data.for.fitting[which(observed.data.for.fitting$id==i),],aes(x = time.since.peak,y = titer),alpha = 0.8,size = 1.5,color="#F46D43") +
geom_line(data=fit.dat.cloud.both, aes(x = time, y = titer, group = samp, col = Serovar ),alpha=0.1,size=0.3) +
geom_line(data=fit.dat.both,aes(x = time, y = titer,col=Serovar),size=1.75,alpha=1) +
geom_point(data = cur.observed.data.for.fitting[which(cur.observed.data.for.fitting$id==i),],aes(x = time.since.peak,y = titer,shape=Serovar),size=3,alpha = 0.65,color="black") +
scale_color_manual(name = "Serovar",labels = c("Pomona","Autumnalis"),values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
scale_shape_manual(name = "Serovar",labels = c("Pomona","Autumnalis"),values = c("Pomona" = 16,"Autumnalis" = 17)) +
scale_y_continuous(limits = c(0,13),breaks=0:13) +
#ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = c(0.75,0.82),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
) +
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (log2 dilution)")
# individual 2
i = observed.data.for.fitting$id[which(observed.data.for.fitting$Pittag==pittag.to.plot[2])][1]
chains.current.individual.df = chains.burn.df[,c(paste0("peak.titer.pomona.",i),paste0("peak.titer.aut.",i),paste0("decay.rate.pomona.",i),paste0("decay.rate.aut.",i),paste0("toi.",i),"iteration","chain")]
colnames(chains.current.individual.df) = c("peak.titer.pomona","peak.titer.aut","decay.rate.pomona","decay.rate.aut","toi","iteration","chain")
cur.peak.titer.pomona = round(mean(chains.current.individual.df[,"peak.titer.pomona"]),1)
dens = density(chains.current.individual.df[,"peak.titer.pomona"])
hpd.int.peak.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.peak.pomona.low = round(hpd.int.peak.pomona[1])
cur.95.peak.pomona.high = round(hpd.int.peak.pomona[2])
hpd.int.peak.pomona = HDInterval::hdi(dens,credMass = 0.5,allowSplit=F)
cur.50.peak.pomona.low = round(hpd.int.peak.pomona[1])
cur.50.peak.pomona.high = round(hpd.int.peak.pomona[2])
cur.decay.pomona = round(mean(chains.current.individual.df[,"decay.rate.pomona"]),6)
dens = density(chains.current.individual.df[,"decay.rate.pomona"])
hpd.int.decay.pomona = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.decay.pomona.low = hpd.int.decay.pomona[1]
cur.95.decay.pomona.high = hpd.int.decay.pomona[2]
hpd.int.decay.pomona = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F)
cur.50.decay.pomona.low = hpd.int.decay.pomona[1]
cur.50.decay.pomona.high = hpd.int.decay.pomona[2]
cur.peak.titer.aut = round(mean(chains.current.individual.df[,"peak.titer.aut"]),1)
dens = density(chains.current.individual.df[,"peak.titer.aut"])
hpd.int.peak.aut = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.peak.aut.low = round(hpd.int.peak.aut[1])
cur.95.peak.aut.high = round(hpd.int.peak.aut[2])
hpd.int.peak.aut = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F)
cur.50.peak.aut.low = round(hpd.int.peak.aut[1])
cur.50.peak.aut.high = round(hpd.int.peak.aut[2])
cur.decay.aut = round(mean(chains.current.individual.df[,"decay.rate.aut"]),6)
dens = density(chains.current.individual.df[,"decay.rate.aut"])
hpd.int.decay.aut = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.decay.aut.low = hpd.int.decay.aut[1]
cur.95.decay.aut.high = hpd.int.decay.aut[2]
hpd.int.decay.aut = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F)
cur.50.decay.aut.low = hpd.int.decay.aut[1]
cur.50.decay.aut.high = hpd.int.decay.aut[2]
dens = density(chains.current.individual.df[,"toi"],bw = 5,from = neg.intervals[i],to=0)
cur.toi = round(dens$x[which.max(dens$y)])
hpd.int.toi = HDInterval::hdi(dens,credMass = 0.95,allowSplit=F)
cur.95.low = round(hpd.int.toi[1])
cur.95.high = round(hpd.int.toi[2])
hpd.int.toi = HDInterval::hdi(dens,credMass = 0.50,allowSplit=F)
cur.50.low = round(hpd.int.toi[1])
cur.50.high = round(hpd.int.toi[2])
plot2a = ggplot(data=chains.current.individual.df,aes(x=toi)) +
geom_density(color=brewer.pal(4,"Spectral")[2],fill=brewer.pal(4,"Spectral")[2],alpha=0.6,size=1) +
geom_vline(xintercept = cur.toi,col="black",size=0.8,alpha=0.6) +
geom_vline(xintercept = cur.95.low,col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept = cur.95.high,col="black",size=0.8,linetype="dotted",alpha=0.6) +
geom_vline(xintercept = cur.50.low,col="black",size=0.8,linetype=2,alpha=0.6) +
geom_vline(xintercept = cur.50.high,col="black",size=0.8,linetype=2,alpha=0.6) +
theme_light(base_family = "Avenir Next") +
#ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("\u03B8 (days)") +
ylab("Posterior density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
#legend.position = c(0.78,0.85),
legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
) +
labs(tag = "(B)")
plot2b = ggplot(data = data.frame(x = 0:15),aes(x=x)) +
stat_function(fun = dnorm, args = list(mean = cur.peak.titer.pomona,sd = sd(chains.current.individual.df$peak.titer.pomona)),aes(col="Pomona"),size=1.5) +
#geom_vline(xintercept = cur.peak.titer.pomona,col=brewer.pal(11,"Spectral")[10]) +
stat_function(fun = dnorm, args = list(mean = cur.peak.titer.aut,sd = sd(chains.current.individual.df$peak.titer.aut)),aes(col="Autumnalis"),size=1.5) +
#geom_vline(xintercept = cur.peak.titer.aut,col=brewer.pal(11,"Spectral")[1]) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
theme_light(base_family = "Avenir Next") +
#ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("Peak antibody level (log2 dilution)") +
ylab("Posterior density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = c(0.2,0.8),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
)
plot2c = ggplot(data = data.frame(x = seq(-0.00015,0.002,0.0001)),aes(x=x)) +
stat_function(fun = dnorm, args = list(mean = cur.decay.pomona,sd = sd(chains.current.individual.df$decay.rate.pomona)),aes(col="Pomona"),size=1.5) +
#geom_vline(xintercept = cur.decay.pomona,col=brewer.pal(11,"Spectral")[10]) +
stat_function(fun = dnorm, args = list(mean = cur.decay.aut,sd = sd(chains.current.individual.df$decay.rate.aut)),aes(col="Autumnalis"),size=1.5) +
#geom_vline(xintercept = cur.peak.titer.aut,col=brewer.pal(11,"Spectral")[1]) +
scale_color_manual("Serovar",values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
theme_light(base_family = "Avenir Next") +
#ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
xlab("Decay rate (1/day)") +
ylab("Posterior density") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = c(0.15,0.8),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
)
# pomona
# sample N values from posterior distribution to get sense of error
N.samp = 200
set.seed(1234);peak.titer.samp.pomona = sample(chains.current.individual.df$peak.titer.pomona[which(chains.current.individual.df$peak.titer.pomona > cur.95.peak.pomona.low & chains.current.individual.df$peak.titer.pomona < cur.95.peak.pomona.high)], size = N.samp, replace = T)
set.seed(1234);decay.rate.samp.pomona = sample(chains.current.individual.df$decay.rate.pomona[which(chains.current.individual.df$decay.rate.pomona > cur.95.decay.pomona.low & chains.current.individual.df$decay.rate.pomona < cur.95.decay.pomona.high)], size = N.samp, replace = T)
set.seed(1234);peak.titer.samp.aut = sample(chains.current.individual.df$peak.titer.aut[which(chains.current.individual.df$peak.titer.aut > cur.95.peak.aut.low & chains.current.individual.df$peak.titer.aut < cur.95.peak.aut.high)], size = N.samp, replace = T)
set.seed(1234);decay.rate.samp.aut = sample(chains.current.individual.df$decay.rate.aut[which(chains.current.individual.df$decay.rate.aut > cur.95.decay.aut.low & chains.current.individual.df$decay.rate.aut < cur.95.decay.aut.high)], size = N.samp, replace = T)
plot.times = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)])
fit.dat.cloud.pomona = data.frame(time = rep(plot.times,N.samp), samp = rep(1:N.samp,each = length(plot.times)), titer = NA,Serovar = "Pomona")
fit.dat.cloud.aut = data.frame(time = rep(plot.times,N.samp), samp = rep(1:N.samp,each = length(plot.times)), titer = NA,Serovar = "Autumnalis")
for(ii in 1:N.samp){
fit.dat.cloud.pomona$titer[which(fit.dat.cloud.pomona$samp == ii)] = exp.fun(start.titer = peak.titer.samp.pomona[ii], rate = -decay.rate.samp.pomona[ii], time = plot.times)
fit.dat.cloud.aut$titer[which(fit.dat.cloud.aut$samp == ii)] = exp.fun(start.titer = peak.titer.samp.aut[ii], rate = -decay.rate.samp.aut[ii], time = plot.times)
}
fit.dat.cloud.both = rbind(fit.dat.cloud.pomona,fit.dat.cloud.aut)
fit.dat.cloud.both$samp = paste(fit.dat.cloud.both$Serovar,fit.dat.cloud.both$samp)
fit.dat.cloud.both$Serovar = factor(fit.dat.cloud.both$Serovar,levels = c("Autumnalis","Pomona"))
fit.dat.pomona = exp.fun(start.titer = cur.peak.titer.pomona, rate = -cur.decay.pomona, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]))
fit.dat.aut = exp.fun(start.titer = cur.peak.titer.aut, rate = -cur.decay.aut, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]))
fit.dat.pomona = data.frame(titer = fit.dat.pomona, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]),Serovar = "Pomona")
fit.dat.aut = data.frame(titer = fit.dat.aut, time = 0:max(observed.data.for.fitting$time.since.peak[which(observed.data.for.fitting$id==i)]),Serovar = "Autumnalis")
fit.dat.both = rbind(fit.dat.pomona,fit.dat.aut)
cur.observed.data.for.fitting = gather(observed.data.for.fitting,Serovar,titer,c("titer.pomona","titer.aut"))
cur.observed.data.for.fitting$Serovar[which(cur.observed.data.for.fitting$Serovar=="titer.pomona")] = "Pomona"
cur.observed.data.for.fitting$Serovar[which(cur.observed.data.for.fitting$Serovar=="titer.aut")] = "Autumnalis"
# cur.observed.data.for.fitting$Serovar = factor(cur.observed.data.for.fitting$Serovar,levels = c("Pomona","Autumnalis"))
plot2d = ggplot() +
#geom_line(data = observed.data.for.fitting[which(observed.data.for.fitting$id==i),],aes(x = time.since.peak,y = titer),alpha = 0.8,size = 1.5,color="#F46D43") +
geom_line(data=fit.dat.cloud.both, aes(x = time, y = titer, group = samp, col = Serovar ),alpha=0.1,size=0.3) +
geom_line(data=fit.dat.both,aes(x = time, y = titer,col=Serovar),size=1.75,alpha=1) +
geom_point(data = cur.observed.data.for.fitting[which(cur.observed.data.for.fitting$id==i),],aes(x = time.since.peak,y = titer,shape=Serovar),size=3,alpha = 0.65,color="black") +
scale_color_manual(name = "Serovar",labels = c("Pomona","Autumnalis"),values = c("Pomona" = brewer.pal(11,"Spectral")[10],"Autumnalis" = brewer.pal(11,"Spectral")[1])) +
scale_shape_manual(name = "Serovar",labels = c("Pomona","Autumnalis"),values = c("Pomona" = 16,"Autumnalis" = 17)) +
scale_y_continuous(limits = c(0,13),breaks=0:13) +
#ggtitle(paste0("Individual ",model.outputs[i,"pittag"])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 13),
text=element_text(size=13),
axis.text=element_text(size=13),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
legend.position = c(0.3,0.2),
#legend.position = "none",
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.background = element_rect(colour = "transparent", fill = "transparent"),
legend.key = element_rect(fill = NA)
) +
xlab("Time since peak antibody level (days)") +
ylab("Antibody level (log2 dilution)")
plot_fig4 = plot1a + plot1b + plot1d +
plot2a + plot2b + plot2d
plot_fig4
3.5 Individual posteriors for seroconversion time
Using full posterior density, 80% CrI and 50% CrI:
#plot.order = rev(order(model.outputs$toi.estimated.maxdens)) # order by maxdens
plot.order = rev(order(neg.intervals))
theta.all.ordered = as.data.frame(theta.all[,plot.order]) %>%
gather
theta.all.ordered$key = rep(1:N.inds, each = 6000)
colnames(theta.all.ordered) = c("id","toi")
theta.all.ordered$toi50 = theta.all.ordered$toi
theta.all.ordered$toi80 = theta.all.ordered$toi
theta.all.ordered$toi95 = theta.all.ordered$toi
for(i in 1:N.inds){
toi.sorted = sort(theta.all.ordered$toi[which(theta.all.ordered$id==unique(theta.all.ordered$id)[i])])
toi.50ci.low = toi.sorted[floor(0.25 * length(toi.sorted))]
toi.50ci.hi = toi.sorted[floor(0.75 * length(toi.sorted))]
theta.all.ordered$toi50[which(theta.all.ordered$id==unique(theta.all.ordered$id)[i] & (theta.all.ordered$toi < toi.50ci.low | theta.all.ordered$toi > toi.50ci.hi))] = NA
toi.80ci.low = toi.sorted[floor(0.1 * length(toi.sorted))]
toi.80ci.hi = toi.sorted[floor(0.9 * length(toi.sorted))]
theta.all.ordered$toi80[which(theta.all.ordered$id==unique(theta.all.ordered$id)[i] & (theta.all.ordered$toi < toi.80ci.low | theta.all.ordered$toi > toi.80ci.hi))] = NA
toi.95ci.low = toi.sorted[floor(0.025 * length(toi.sorted))]
toi.95ci.hi = toi.sorted[floor(0.975 * length(toi.sorted))]
theta.all.ordered$toi95[which(theta.all.ordered$id==unique(theta.all.ordered$id)[i] & (theta.all.ordered$toi < toi.95ci.low | theta.all.ordered$toi > toi.95ci.hi))] = NA
}
# divide into 4 parts for plotting
theta.all.ordered.part1 = theta.all.ordered[which(theta.all.ordered$id %in% 1:floor(N.inds/4)),]
theta.all.ordered.part2 = theta.all.ordered[which(theta.all.ordered$id %in% (floor(N.inds/4)+1):(floor(N.inds/4)*2)),]
theta.all.ordered.part3 = theta.all.ordered[which(theta.all.ordered$id %in% ((floor(N.inds/4)*2)+1):(floor(N.inds/4)*3)),]
theta.all.ordered.part4 = theta.all.ordered[which(theta.all.ordered$id %in% ((floor(N.inds/4)*3)+1):N.inds),]
individual.theta.plots.1 = ggplot() +
geom_density_ridges(data = theta.all.ordered.part1,aes(x = toi,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[10],alpha=0.8) +
geom_density_ridges(data = theta.all.ordered.part1,aes(x = toi80,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[2],alpha=0.8) +
geom_density_ridges(data = theta.all.ordered.part1,aes(x = toi50,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[5],alpha=0.8) +
coord_flip() +
ylab("ID") +
xlab("\u03B8 (days)") +
#ggtitle("1/4") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.text.x = element_blank(),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)))
individual.theta.plots.2 = ggplot() +
geom_density_ridges(data = theta.all.ordered.part2,aes(x = toi,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[10],alpha=0.8) +
geom_density_ridges(data = theta.all.ordered.part2,aes(x = toi80,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[2],alpha=0.8) +
geom_density_ridges(data = theta.all.ordered.part2,aes(x = toi50,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[5],alpha=0.8) +
coord_flip() +
ylab("ID") +
xlab("\u03B8 (days)") +
#ggtitle("Peak titer time (2/4)") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.text.x = element_blank(),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)) )
individual.theta.plots.3 = ggplot() +
geom_density_ridges(data = theta.all.ordered.part3,aes(x = toi,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[10],alpha=0.8) +
geom_density_ridges(data = theta.all.ordered.part3,aes(x = toi80,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[2],alpha=0.8) +
geom_density_ridges(data = theta.all.ordered.part3,aes(x = toi50,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[5],alpha=0.8) +
coord_flip() +
ylab("ID") +
xlab("\u03B8 (days)") +
#ggtitle("Peak titer time (3/4)") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.text.x = element_blank(),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)) )
individual.theta.plots.4 = ggplot() +
geom_density_ridges(data = theta.all.ordered.part4,aes(x = toi,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[10],alpha=0.8) +
geom_density_ridges(data = theta.all.ordered.part4,aes(x = toi80,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[2],alpha=0.8) +
geom_density_ridges(data = theta.all.ordered.part4,aes(x = toi50,y = factor(id),height=..density..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[5],alpha=0.8) +
coord_flip() +
ylab("ID") +
xlab("\u03B8 (days)") +
#ggtitle("Peak titer time (4/4)") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.text.x = element_blank(),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)) )
individual.theta.plots.all = individual.theta.plots.1 /
individual.theta.plots.2 /
individual.theta.plots.3 /
individual.theta.plots.4
individual.theta.plots.all
Using full posterior density, 95% CrI and 50% CrI:
individual.theta.plots.1 = ggplot() +
geom_density_ridges(data = theta.all.ordered.part1,aes(x = toi,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[10],alpha=0.8, scale = 0.8) +
geom_density_ridges(data = theta.all.ordered.part1,aes(x = toi95,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[2],alpha=0.8, scale = 0.8) +
geom_density_ridges(data = theta.all.ordered.part1,aes(x = toi50,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[5],alpha=0.8, scale = 0.8) +
coord_flip() +
ylab("ID") +
xlab("\u03B8 (days)") +
#ggtitle("1/4") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.text.x = element_blank(),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)))
individual.theta.plots.2 = ggplot() +
geom_density_ridges(data = theta.all.ordered.part2,aes(x = toi,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[10],alpha=0.8, scale = 0.8) +
geom_density_ridges(data = theta.all.ordered.part2,aes(x = toi95,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[2],alpha=0.8, scale = 0.8) +
geom_density_ridges(data = theta.all.ordered.part2,aes(x = toi50,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[5],alpha=0.8, scale = 0.8) +
coord_flip() +
ylab("ID") +
xlab("\u03B8 (days)") +
#ggtitle("Peak titer time (2/4)") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.text.x = element_blank(),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)) )
individual.theta.plots.3 = ggplot() +
geom_density_ridges(data = theta.all.ordered.part3,aes(x = toi,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[10],alpha=0.8, scale = 0.8) +
geom_density_ridges(data = theta.all.ordered.part3,aes(x = toi95,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[2],alpha=0.8, scale = 0.8) +
geom_density_ridges(data = theta.all.ordered.part3,aes(x = toi50,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[5],alpha=0.8, scale = 0.8) +
coord_flip() +
ylab("ID") +
xlab("\u03B8 (days)") +
#ggtitle("Peak titer time (3/4)") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.text.x = element_blank(),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)) )
individual.theta.plots.4 = ggplot() +
geom_density_ridges(data = theta.all.ordered.part4,aes(x = toi,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[10],alpha=0.8, scale = 0.8) +
geom_density_ridges(data = theta.all.ordered.part4,aes(x = toi95,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[2],alpha=0.8, scale = 0.8) +
geom_density_ridges(data = theta.all.ordered.part4,aes(x = toi50,y = factor(id),height=..scaled..), stat = "density",size=0.1,trim=T,fill = brewer.pal(11,"Spectral")[5],alpha=0.8, scale = 0.8) +
coord_flip() +
ylab("ID") +
xlab("\u03B8 (days)") +
#ggtitle("Peak titer time (4/4)") +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.text.x = element_blank(),
axis.title.y = element_text(margin = ggplot2::margin(r = 10)) )
individual.theta.plots.all = individual.theta.plots.1 /
individual.theta.plots.2 /
individual.theta.plots.3 /
individual.theta.plots.4
individual.theta.plots.all
# ggsave(plot = individual.theta.plots.all,filename = "/Users/bennyborremans/Documents/Werk/Manuscripten/Eerste auteur/ongoing/Fox titer kinetics/Figures/Figure_posterior_toi_all_individuals_incl95_50cri.png",width = 9, height = 12, dpi=600)3.6 Mean information gained
3.6.1 Using only individuals for which there is at least one Autumnalis
result
# exclude individuals without any Aut samples
model.outputs.aut.not.all.na = model.outputs[id.aut.not.all.na,]Mean amount of information gained (% reduction of seroconversion
interval):
95% CrI:
0.0606345
(standard error = 0.005447)
Range: 0.0050469, 0.6477495
Median:
0.038006
80% CrI: 0.1433464
(standard error = 0.0085034)
Range: 0.0166341, 0.7676125 Median:
0.109589
50% CrI: 0.2216634
(standard error = 0.0108736)
Range: 0.0254403, 0.8317025 Median:
0.1937378
## KL-divergence
3.6.2 Using only individuals for which there is at least one Autumnalis
result
Mean amount of information gained (KL-divergence):
0.1077575
(standard error = 0.0136857)
Range: 0.0061715, 1.6953464
Median: 0.1077575
Histogram:
plot.seroconv.red = ggplot(data = model.outputs.aut.not.all.na, aes(x = toi.information.gained.95cri.perc)) +
geom_histogram(bins=12,binwidth = 0.01,fill="#9E0142",col="black",aes(y = ..density..)) +
theme_classic(base_family = "Avenir Next") +
scale_x_continuous(limits = c(0,0.6)) +
scale_color_manual("Distribution",values = c("N(6,2.2)" = "#66C2A5","N(7,3)" = "#FDAE61")) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Seroconversion interval reduction (%)") +
ylab("Density")
plot.seroconv.red
# ggsave(plot = plot.seroconv.red,filename = "/Users/bennyborremans/Documents/Werk/Manuscripten/Eerste auteur/ongoing/Fox titer kinetics/Figures/SI_figure_seroconversion_interval_reduction_at_least_one_aut_pos_histogram.png",width = 4, height = 4, dpi=600)3.7 Mean information gained
3.7.1 Using all individuals
Mean amount of information gained (% reduction of seroconversion
interval):
95% CrI: 0.0609136
(standard error = 0.0042255)
Range: 9.2697497^{-4}, 0.6477495 Median:
0.038006
80% CrI: 0.1439551
(standard error = 0.0068252)
Range: 0.0092955, 0.7676125 Median:
0.109589
50% CrI: 0.2227031
(standard error = 0.0087855)
Range: 0.0254403, 0.8317025 Median:
0.18591
3.8 KL-divergence
3.8.1 Using all individuals
Mean amount of information gained (KL-divergence):
0.1074396
(standard error = 0.0103598)
Range: 0.0061715, 1.6953464
Median:
0.1074396
Histogram:
95% CrI:
plot.seroconv.red = ggplot(data = model.outputs, aes(x = toi.information.gained.95cri.perc)) +
geom_histogram(bins=12,binwidth = 0.01,fill="#9E0142",col="black",aes(y = ..density..)) +
theme_classic(base_family = "Avenir Next") +
scale_x_continuous(breaks = seq(0,1,0.1)) +
scale_color_manual("Distribution",values = c("N(6,2.2)" = "#66C2A5","N(7,3)" = "#FDAE61")) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Seroconversion interval reduction (%)") +
ylab("Density")
plot.seroconv.red
# ggsave(plot = plot.seroconv.red,filename = "/Users/bennyborremans/Documents/Werk/Manuscripten/Eerste auteur/ongoing/Fox titer kinetics/Figures/SI_figure_seroconversion_interval_reduction_95cri_all_samples_histogram.png",width = 4, height = 4, dpi=600)80% CrI:
plot.seroconv.red = ggplot(data = model.outputs, aes(x = toi.information.gained.80cri.perc)) +
geom_histogram(bins=12,binwidth = 0.01,fill="#9E0142",col="black",aes(y = ..density..)) +
theme_classic(base_family = "Avenir Next") +
scale_x_continuous(breaks = seq(0,1,0.1)) +
scale_color_manual("Distribution",values = c("N(6,2.2)" = "#66C2A5","N(7,3)" = "#FDAE61")) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Seroconversion interval reduction (%)") +
ylab("Density")
plot.seroconv.red
# ggsave(plot = plot.seroconv.red,filename = "/Users/bennyborremans/Documents/Werk/Manuscripten/Eerste auteur/ongoing/Fox titer kinetics/Figures/SI_figure_seroconversion_interval_reduction_80cri_all_samples_histogram.png",width = 4, height = 4, dpi=600)50% CrI:
plot.seroconv.red = ggplot(data = model.outputs, aes(x = toi.information.gained.50cri.perc)) +
geom_histogram(bins=12,binwidth = 0.01,fill="#9E0142",col="black",aes(y = ..density..)) +
theme_classic(base_family = "Avenir Next") +
scale_x_continuous(breaks = seq(0,1,0.1)) +
scale_color_manual("Distribution",values = c("N(6,2.2)" = "#66C2A5","N(7,3)" = "#FDAE61")) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Seroconversion interval reduction (%)") +
ylab("Density")
plot.seroconv.red
# ggsave(plot = plot.seroconv.red,filename = "/Users/bennyborremans/Documents/Werk/Manuscripten/Eerste auteur/ongoing/Fox titer kinetics/Figures/SI_figure_seroconversion_interval_reduction_50cri_all_samples_histogram.png",width = 4, height = 4, dpi=600)3.9 Correlation between interval size reduction and KL-divergence
95% CrI:
ggplot(model.outputs,aes(x = toi.information.gained.95cri.perc, y = kl.divergence)) +
geom_point(color = brewer.pal(11,"Spectral")[2],size = 0.5) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Seroconversion interval reduction (%)") +
ylab("Relative entropy (bits)")
80% CrI:
ggplot(model.outputs,aes(x = toi.information.gained.80cri.perc, y = kl.divergence)) +
geom_point(color = brewer.pal(11,"Spectral")[2],size = 0.5) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Seroconversion interval reduction (%)") +
ylab("Relative entropy (bits)")
50% CrI:
ggplot(model.outputs,aes(x = toi.information.gained.50cri.perc, y = kl.divergence)) +
geom_point(color = brewer.pal(11,"Spectral")[2],size = 0.5) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Seroconversion interval reduction (%)") +
ylab("Relative entropy (bits)")
3.10 Correlates of model performance
Why does the model result in better estimates of seroconversion time for some individuals than for others?
3.10.1 Seroconversion interval size
All for 95% CrI
model.outputs$interval.size = abs(neg.intervals)
ggplot(model.outputs,aes(x = interval.size, y = 100*toi.information.gained.95cri.perc)) +
geom_point(color = brewer.pal(11,"Spectral")[2],size = 0.7) +
scale_y_continuous(limits=c(0,65)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
)+
xlab("Seroconversion interval size (days)") +
ylab("Interval reduction (%)")
# lm.interval.size = lm(log(toi.information.gained.95cri.perc) ~ scale(interval.size),data = model.outputs)
lm.interval.size = brm(log(toi.information.gained.95cri.perc) ~ scale(interval.size),data = model.outputs)3.10.2 Range of time covered by datapoints
model.outputs$datarange = NA
for(i in 1:nrow(model.outputs)){
model.outputs$datarange[i] = max(observed.data.for.fitting$time[which(observed.data.for.fitting$id == i)])
}
ggplot(model.outputs,aes(x = datarange, y = 100*toi.information.gained.95cri.perc)) +
geom_point(color = brewer.pal(11,"Spectral")[2],size = 0.5) +
scale_y_continuous(limits=c(0,65)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Range of datapoints (days)") +
ylab("Interval reduction (%)")
# lm.datarange = lm(log(toi.information.gained.95cri.perc) ~ scale(datarange),data = model.outputs)
lm.datarange = brm(log(toi.information.gained.95cri.perc) ~ scale(datarange),data = model.outputs)3.10.3 Number of datapoints
model.outputs$number.of.datapoints = NA
for(i in 1:nrow(model.outputs)){
model.outputs$number.of.datapoints[i] = sum(observed.data.for.fitting$id == i)
}
ggplot(model.outputs,aes(x = number.of.datapoints, y = 100*toi.information.gained.95cri.perc)) +
geom_point(color = brewer.pal(11,"Spectral")[2],size = 0.5) +
scale_y_continuous(limits = c(0,65)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Number of datapoints") +
ylab("Interval reduction (%)")
#lm.number.of.datapoints = lm(log(toi.information.gained.95cri.perc) ~ scale(number.of.datapoints),data = model.outputs)
lm.number.of.datapoints = brm(log(toi.information.gained.95cri.perc) ~ scale(number.of.datapoints),data = model.outputs)3.10.4 Estimated peak level
ggplot(model.outputs,aes(x = peak.titer.estimated.mean.pomona, y = 100*toi.information.gained.95cri.perc)) +
geom_point(color = brewer.pal(11,"Spectral")[2],size = 0.5) +
scale_y_continuous(limits = c(0,65)) +
scale_x_continuous(breaks = seq(0,15,2)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Estimated peak antibody level \n(log2 dilution)") +
ylab("Interval reduction (%)")
#lm.estimated.peak.level = lm(log(toi.information.gained.95cri.perc) ~ scale(peak.titer.estimated.mean.pomona),data = model.outputs)
lm.estimated.peak.level = brm(log(toi.information.gained.95cri.perc) ~ scale(peak.titer.estimated.mean.pomona),data = model.outputs)3.10.5 Antibody level of the first positive sample
model.outputs$first.pos.level = observed.data.for.fitting$titer.pomona[which(observed.data.for.fitting$time==0)]
# unique(observed.data.for.fitting$Pittag) %in% model.outputs$pittag
ggplot(model.outputs,aes(x = first.pos.level, y = 100*toi.information.gained.95cri.perc)) +
geom_jitter(color = brewer.pal(11,"Spectral")[2],size = 0.7,width=0.1) +
scale_x_continuous(breaks=seq(0,15,2)) +
scale_y_continuous(limits=c(0,65)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
)+
xlab("Pomona level first positive sample \n(log2 dilutions)") +
ylab("Interval reduction (%)")
#lm.first.pos.level = lm(log(toi.information.gained.95cri.perc) ~ scale(first.pos.level),data = model.outputs)
lm.first.pos.level = brm(log(toi.information.gained.95cri.perc) ~ scale(first.pos.level),data = model.outputs)3.10.6 Estimated decay rate
ggplot(model.outputs,aes(x = decay.rate.estimated.mean.pomona, y = 100*toi.information.gained.95cri.perc)) +
geom_point(color = brewer.pal(11,"Spectral")[2],size = 0.5) +
scale_y_continuous(limits = c(0,65)) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Estimated decay rate") +
ylab("Interval reduction (%)")
#lm.decay.rate = lm(log(toi.information.gained.95cri.perc) ~ scale(decay.rate.estimated.mean.pomona),data = model.outputs)
lm.decay.rate = brm(log(toi.information.gained.95cri.perc) ~ scale(decay.rate.estimated.mean.pomona),data = model.outputs)3.10.7 Estimated decay rate and estimate peak antibody level
ggplot(model.outputs,aes(x = decay.rate.estimated.mean.pomona, y = peak.titer.estimated.mean.pomona,color = 100*toi.information.gained.95cri.perc)) +
geom_point(size = 1) +
scale_y_continuous(breaks = seq(0,15,2)) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Estimated decay rate") +
ylab("Estimated peak level")
#lm.decay.rate.and.estimated.peak.level = lm(log(toi.information.gained.95cri.perc) ~ scale(decay.rate.estimated.mean.pomona) + scale(peak.titer.estimated.mean.pomona),data = model.outputs)
lm.decay.rate.and.estimated.peak.level = brm(log(toi.information.gained.95cri.perc) ~ scale(decay.rate.estimated.mean.pomona) + scale(peak.titer.estimated.mean.pomona),data = model.outputs)3.10.8 Estimated decay rate and first positive level
ggplot(model.outputs,aes(x = decay.rate.estimated.mean.pomona, y = first.pos.level,color = 100*toi.information.gained.95cri.perc)) +
geom_point(size = 1) +
scale_y_continuous(breaks = seq(0,15,2)) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Estimated decay rate") +
ylab("First positive sample level")
#lm.decay.rate.and.first.positive.level = lm(log(toi.information.gained.95cri.perc) ~ scale(decay.rate.estimated.mean.pomona) + scale(first.pos.level),data = model.outputs)
lm.decay.rate.and.first.positive.level = brm(log(toi.information.gained.95cri.perc) ~ scale(decay.rate.estimated.mean.pomona) + scale(first.pos.level),data = model.outputs)3.10.9 Interval size and decay rate
ggplot(model.outputs,aes(x = decay.rate.estimated.mean.pomona, y = interval.size,color = 100*toi.information.gained.95cri.perc)) +
geom_point(size = 1) +
#scale_y_continuous(breaks = seq(0,15,2)) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Estimated decay rate (1/day)") +
ylab("Seroconversion interval size (days)")
lm.decay.rate.and.interval.size = brm(log(toi.information.gained.95cri.perc) ~ scale(decay.rate.estimated.mean.pomona) + scale(interval.size),data = model.outputs)3.10.10 Interval size and peak level
ggplot(model.outputs,aes(x = interval.size, y = peak.titer.estimated.mean.pomona,color = 100*toi.information.gained.95cri.perc)) +
geom_point(size = 1) +
scale_y_continuous(breaks = seq(0,15,2)) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Seroconversion interval size (days)") +
ylab("Estimated peak level (log2 dilution)")
lm.peaklevel.and.interval.size = brm(log(toi.information.gained.95cri.perc) ~ scale(peak.titer.estimated.mean.pomona) + scale(interval.size),data = model.outputs)3.10.11 Estimated decay, estimated peak, interval size
plot.interval.decay = ggplot(model.outputs,aes(x = interval.size, y = decay.rate.estimated.mean.pomona,color = 100*toi.information.gained.95cri.perc)) +
geom_point(size = 1) +
#scale_y_continuous(breaks = seq(0,15,2)) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = 'none'
) +
ylab("Estimated decay rate (1/day)") +
xlab("Infection window (days)")
plot.interval.peak = ggplot(model.outputs,aes(x = interval.size, y = peak.titer.estimated.mean.pomona,color = 100*toi.information.gained.95cri.perc)) +
geom_point(size = 1) +
scale_y_continuous(breaks = seq(0,15,2)) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = 'none'
) +
xlab("Infection window (days)") +
ylab("Estimated peak level \n(log2 dilution)")
plot.interval.firstpos = ggplot(model.outputs,aes(x = interval.size, y = first.pos.level,color = 100*toi.information.gained.95cri.perc)) +
geom_point(size = 1) +
scale_y_continuous(breaks = seq(0,15,2)) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm"),
legend.position = 'none'
) +
xlab("Infection window (days)") +
ylab("First positive Pomona sample \nlevel (log2 dilution)")
plot.firstpos.decay = ggplot(model.outputs,aes(x = decay.rate.estimated.mean.pomona, y = first.pos.level,color = 100*toi.information.gained.95cri.perc)) +
geom_point(size = 1) +
scale_y_continuous(breaks = seq(0,15,2)) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Estimated decay rate (1/day)") +
ylab("First positive Pomona sample \nlevel (log2 dilution)")
plot.covariates.reduction = plot.interval.decay + plot.interval.firstpos + plot.firstpos.decay
plot.covariates.reduction
3.10.12 Number of data points and first positive level
ggplot(model.outputs,aes(x = number.of.datapoints, y = first.pos.level,color = 100*toi.information.gained.95cri.perc)) +
geom_jitter(size = 1,width=0.1) +
scale_x_continuous(breaks = 0:10) +
scale_y_continuous(breaks = 0:15) +
scale_color_gradientn(name = "Interval \nreduction \n(%)",colors = rev(brewer.pal(11,"Spectral")[c(1,2,3,5,8,10,11)])) +
theme_light(base_family = "Avenir Next") +
theme(plot.title = element_text(size = 11),
text=element_text(size=11),
axis.text=element_text(size=10),
axis.title.y = element_text(margin = ggplot2::margin(r=10)),
plot.margin = unit(c(0.5,0.75,0.5,0.5),"cm")
) +
xlab("Number of positive datapoints") +
ylab("First positive sample level \n(log2 dilution)")
#lm.number.of.datapoints.and.first.positive.level = lm(log(toi.information.gained.95cri.perc) ~ scale(number.of.datapoints) + scale(first.pos.level),data = model.outputs)
lm.number.of.datapoints.and.first.positive.level = brm(log(toi.information.gained.95cri.perc) ~ scale(number.of.datapoints) + scale(first.pos.level),data = model.outputs)4 All covariates
lm.all = brm(log(toi.information.gained.95cri.perc) ~ scale(first.pos.level) + scale(decay.rate.estimated.mean.pomona) + scale(peak.titer.estimated.mean.pomona) + scale(interval.size) + scale(datarange) + scale(number.of.datapoints), data = model.outputs)4.0.1 Model comparison
4.0.1.1 Univariate models P values
# frequentist:
# lm.interval.size
# lm.datarange
# lm.number.of.datapoints
# lm.estimated.peak.level
# lm.first.pos.level
# lm.decay.rate
# lm.decay.rate.and.estimated.peak.level
# lm.decay.rate.and.first.positive.level
# lm.number.of.datapoints.and.first.positive.level
# model.stats = data.frame(
# variables = c("interval size","datarange","number of datapoints","estimated peak","first pos level","decay rate"),
# Pval = c(anova(lm.interval.size)$Pr[1],anova(lm.datarange)$Pr[1],anova(lm.number.of.datapoints)$Pr[1],anova(lm.estimated.peak.level)$Pr[1],anova(lm.first.pos.level)$Pr[1],anova(lm.decay.rate)$Pr[1]),
# Fval = round(c(anova(lm.interval.size)$F[1],anova(lm.datarange)$F[1],anova(lm.number.of.datapoints)$F[1],anova(lm.estimated.peak.level)$F[1],anova(lm.first.pos.level)$F[1],anova(lm.decay.rate)$F[1]),2),
# df = c(anova(lm.interval.size)$Df[2],anova(lm.datarange)$Df[2],anova(lm.number.of.datapoints)$Df[2],anova(lm.estimated.peak.level)$Df[2],anova(lm.first.pos.level)$Df[2],anova(lm.decay.rate)$Df[2]),
# Effect.estimate.exp = exp(round(c(lm.interval.size$coefficients[2],lm.datarange$coefficients[2],lm.number.of.datapoints$coefficients[2],lm.estimated.peak.level$coefficients[2],lm.first.pos.level$coefficients[2],lm.decay.rate$coefficients[2]),4)),
# AIC = round(AIC(lm.interval.size,lm.datarange,lm.number.of.datapoints,lm.estimated.peak.level,lm.first.pos.level,lm.decay.rate),1))
#
# model.stats$Pval = round(model.stats$Pval,6)
# bayesian models:
model.stats = data.frame(
variables = c("interval size","datarange","number of datapoints","estimated peak","first pos level","decay rate"),
LOOIC.est = round(c(loo(lm.interval.size)$estimates[3,1],
loo(lm.datarange)$estimates[3,1],
loo(lm.number.of.datapoints)$estimates[3,1],
loo(lm.estimated.peak.level)$estimates[3,1],
loo(lm.first.pos.level)$estimates[3,1],
loo(lm.decay.rate)$estimates[3,1]),1),
LOOIC.se = round(c(loo(lm.interval.size)$estimates[3,2],
loo(lm.datarange)$estimates[3,2],
loo(lm.number.of.datapoints)$estimates[3,2],
loo(lm.estimated.peak.level)$estimates[3,2],
loo(lm.first.pos.level)$estimates[3,2],
loo(lm.decay.rate)$estimates[3,2]),1),
Effect.estimate.exp = round(c(exp(summary(lm.interval.size)$fixed[2,1]),
exp(summary(lm.datarange)$fixed[2,1]),
exp(summary(lm.number.of.datapoints)$fixed[2,1]),
exp(summary(lm.estimated.peak.level)$fixed[2,1]),
exp(summary(lm.first.pos.level)$fixed[2,1]),
exp(summary(lm.decay.rate)$fixed[2,1])),2),
Effect.estimate.95lo.exp = round(c(exp(summary(lm.interval.size)$fixed[2,3]),
exp(summary(lm.datarange)$fixed[2,3]),
exp(summary(lm.number.of.datapoints)$fixed[2,3]),
exp(summary(lm.estimated.peak.level)$fixed[2,3]),
exp(summary(lm.first.pos.level)$fixed[2,3]),
exp(summary(lm.decay.rate)$fixed[2,3])),2),
Effect.estimate.95hi.exp = round(c(exp(summary(lm.interval.size)$fixed[2,4]),
exp(summary(lm.datarange)$fixed[2,4]),
exp(summary(lm.number.of.datapoints)$fixed[2,4]),
exp(summary(lm.estimated.peak.level)$fixed[2,4]),
exp(summary(lm.first.pos.level)$fixed[2,4]),
exp(summary(lm.decay.rate)$fixed[2,4])),2))kbl(model.stats) %>%
kable_classic(bootstrap_options = c("striped", "hover","condensed"), full_width=F,html_font="Cambria",fixed_thead=T)| variables | LOOIC.est | LOOIC.se | Effect.estimate.exp | Effect.estimate.95lo.exp | Effect.estimate.95hi.exp |
|---|---|---|---|---|---|
| interval size | 721.5 | 37.0 | 1.28 | 1.17 | 1.40 |
| datarange | 747.6 | 33.5 | 1.09 | 0.99 | 1.19 |
| number of datapoints | 747.6 | 34.1 | 1.09 | 0.99 | 1.20 |
| estimated peak | 743.6 | 31.7 | 1.13 | 1.04 | 1.24 |
| first pos level | 751.1 | 34.2 | 1.01 | 0.92 | 1.11 |
| decay rate | 674.3 | 39.0 | 1.47 | 1.36 | 1.59 |
4.0.1.2 LOOIC values
All for 95% CrI
lm.interval.size.and.decay = brm(log(toi.information.gained.95cri.perc) ~ scale(interval.size) + scale(decay.rate.estimated.mean.pomona),data = model.outputs)
lm.interval.size.and.peak = brm(log(toi.information.gained.95cri.perc) ~ scale(interval.size) + scale(peak.titer.estimated.mean.pomona),data = model.outputs)
lm.interval.size.and.peak.and.decay = brm(log(toi.information.gained.95cri.perc) ~ scale(interval.size) + scale(peak.titer.estimated.mean.pomona) + scale(decay.rate.estimated.mean.pomona),data = model.outputs)
lm.number.of.datapoints.and.peak.and.decay = brm(log(toi.information.gained.95cri.perc) ~ scale(number.of.datapoints) + scale(peak.titer.estimated.mean.pomona) + scale(decay.rate.estimated.mean.pomona),data = model.outputs)
lm.number.of.datapoints.and.peak = brm(log(toi.information.gained.95cri.perc) ~ scale(number.of.datapoints) + scale(peak.titer.estimated.mean.pomona),data = model.outputs)
model.stats = data.frame(
variables = c("interval size",
"datarange",
"number of datapoints",
"estimated peak",
"first pos level",
"decay rate",
"decay rate + estimated peak",
"decay rate + first pos",
"N datapoints + peak",
"N datapoints + decay + peak",
"interval size + decay rate",
"interval size + estimated peak",
"interval size + decay + peak",
"all variables"),
LOOIC.est = round(c(loo(lm.interval.size)$estimates[3,1],
loo(lm.datarange)$estimates[3,1],
loo(lm.number.of.datapoints)$estimates[3,1],
loo(lm.estimated.peak.level)$estimates[3,1],
loo(lm.first.pos.level)$estimates[3,1],
loo(lm.decay.rate)$estimates[3,1],
loo(lm.decay.rate.and.estimated.peak.level)$estimates[3,1],
loo(lm.decay.rate.and.first.positive.level)$estimates[3,1],
loo(lm.number.of.datapoints.and.peak)$estimates[3,1],
loo(lm.number.of.datapoints.and.peak.and.decay)$estimates[3,1],
loo(lm.interval.size.and.decay)$estimates[3,1],
loo(lm.interval.size.and.peak)$estimates[3,1],
loo(lm.interval.size.and.peak.and.decay)$estimates[3,1],
loo(lm.all)$estimates[3,1]),1),
LOOIC.se = round(c(loo(lm.interval.size)$estimates[3,2],
loo(lm.datarange)$estimates[3,2],
loo(lm.number.of.datapoints)$estimates[3,2],
loo(lm.estimated.peak.level)$estimates[3,2],
loo(lm.first.pos.level)$estimates[3,2],
loo(lm.decay.rate)$estimates[3,2],
loo(lm.decay.rate.and.estimated.peak.level)$estimates[3,2],
loo(lm.decay.rate.and.first.positive.level)$estimates[3,2],
loo(lm.number.of.datapoints.and.peak)$estimates[3,2],
loo(lm.number.of.datapoints.and.peak.and.decay)$estimates[3,2],
loo(lm.interval.size.and.decay)$estimates[3,2],
loo(lm.interval.size.and.peak)$estimates[3,2],
loo(lm.interval.size.and.peak.and.decay)$estimates[3,2],
loo(lm.all)$estimates[3,2]),1))
model.stats.ordered = model.stats %>%
arrange(LOOIC.est)kbl(model.stats.ordered) %>%
kable_classic(bootstrap_options = c("striped", "hover","condensed"), full_width=F,html_font="Cambria",fixed_thead=T)| variables | LOOIC.est | LOOIC.se |
|---|---|---|
| interval size + decay + peak | 589.3 | 44.6 |
| all variables | 591.5 | 44.1 |
| interval size + decay rate | 631.2 | 45.7 |
| N datapoints + decay + peak | 648.4 | 36.0 |
| decay rate + estimated peak | 649.5 | 35.8 |
| decay rate | 674.3 | 39.0 |
| decay rate + first pos | 675.9 | 38.4 |
| interval size + estimated peak | 711.1 | 34.3 |
| interval size | 721.5 | 37.0 |
| N datapoints + peak | 743.2 | 31.8 |
| estimated peak | 743.6 | 31.7 |
| datarange | 747.6 | 33.5 |
| number of datapoints | 747.6 | 34.1 |
| first pos level | 751.1 | 34.2 |
4.1 Model output table
kbl(model.outputs) %>%
kable_classic(bootstrap_options = c("striped", "hover","condensed"), full_width=F,html_font="Cambria",fixed_thead=T)| id | pittag | toi.mid.interval | toi.estimated.mean | toi.estimated.median | toi.estimated.maxdens | toi.estimated.mean.95ci.low | toi.estimated.mean.95ci.high | toi.estimated.maxdens.95hdi.low | toi.estimated.maxdens.95hdi.high | toi.information.gained.95cri.perc | toi.information.gained.80cri.perc | toi.information.gained.50cri.perc | kl.divergence | peak.titer.estimated.mean.pomona | peak.titer.estimated.median.pomona | peak.titer.estimated.maxdens.pomona | peak.titer.estimated.mean.95ci.low.pomona | peak.titer.estimated.mean.95ci.high.pomona | peak.titer.estimated.maxdens.95hdi.low.pomona | peak.titer.estimated.maxdens.95hdi.high.pomona | decay.rate.estimated.mean.pomona | decay.rate.estimated.median.pomona | decay.rate.estimated.maxdens.pomona | decay.rate.estimated.mean.95ci.low.pomona | decay.rate.estimated.mean.95ci.high.pomona | decay.rate.estimated.maxdens.95hdi.low.pomona | decay.rate.estimated.maxdens.95hdi.high.pomona | peak.titer.estimated.mean.aut | peak.titer.estimated.median.aut | peak.titer.estimated.maxdens.aut | peak.titer.estimated.mean.95ci.low.aut | peak.titer.estimated.mean.95ci.high.aut | peak.titer.estimated.maxdens.95hdi.low.aut | peak.titer.estimated.maxdens.95hdi.high.aut | decay.rate.estimated.mean.aut | decay.rate.estimated.median.aut | decay.rate.estimated.maxdens.aut | decay.rate.estimated.mean.95ci.low.aut | decay.rate.estimated.mean.95ci.high.aut | decay.rate.estimated.maxdens.95hdi.low.aut | decay.rate.estimated.maxdens.95hdi.high.aut | interval.size | datarange | number.of.datapoints | first.pos.level |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 00141 | -136 | -160 | -171 | -260 | -268 | -178 | -272 | -25 | 0.0441858 | 0.1316047 | 0.2563601 | 0.0762514 | 4.3 | 4.2 | 4.2 | 2.4 | 4.2 | 2.4 | 6.3 | 0.000987 | 0.0009830 | 0.000989 | 0.000357 | 0.000963 | 0.000345 | 0.001626 | 4.5 | 4.4 | 4.5 | 2.1 | 4.4 | 2.1 | 6.9 | 0.000660 | 0.0006560 | 0.000629 | 0.000105 | 0.000639 | 0.000104 | 0.001227 | 272 | 586 | 2 | 3 |
| 2 | 00701 | -362 | -335 | -325 | -287 | -692 | -340 | -665 | -4 | 0.0400659 | 0.0973581 | 0.1545988 | 0.0367747 | 7.0 | 6.9 | 6.6 | 5.0 | 6.8 | 4.8 | 9.4 | 0.000879 | 0.0008680 | 0.000842 | 0.000488 | 0.000854 | 0.000470 | 0.001310 | 7.5 | 7.3 | 7.0 | 5.2 | 7.3 | 5.0 | 10.3 | 0.000756 | 0.0007490 | 0.000751 | 0.000312 | 0.000734 | 0.000302 | 0.001235 | 725 | 1093 | 4 | 5 |
| 3 | 00978 | -115 | -132 | -140 | -217 | -226 | -146 | -230 | -20 | 0.0380060 | 0.1120352 | 0.2172211 | 0.0573983 | 4.8 | 4.8 | 4.7 | 3.2 | 4.7 | 3.1 | 6.7 | 0.001032 | 0.0010280 | 0.001019 | 0.000433 | 0.001009 | 0.000421 | 0.001650 | 5.1 | 5.1 | 4.9 | 2.9 | 5.0 | 2.9 | 7.4 | 0.000659 | 0.0006560 | 0.000672 | 0.000107 | 0.000639 | 0.000106 | 0.001221 | 230 | 629 | 3 | 4 |
| 4 | 00D05 | -126 | -123 | -122 | -17 | -245 | -128 | -241 | -6 | 0.0174065 | 0.0288650 | 0.0410959 | 0.0082026 | 8.9 | 8.9 | 8.9 | 7.4 | 8.9 | 7.4 | 10.5 | 0.000616 | 0.0006100 | 0.000597 | 0.000411 | 0.000603 | 0.000401 | 0.000845 | 9.9 | 9.9 | 9.8 | 8.3 | 9.8 | 8.2 | 11.7 | 0.000398 | 0.0003960 | 0.000387 | 0.000235 | 0.000390 | 0.000231 | 0.000569 | 252 | 2038 | 5 | 7 |
| 5 | 01334 | -247 | -238 | -235 | -177 | -478 | -246 | -468 | -7 | 0.0174065 | 0.0435421 | 0.0684932 | 0.0115655 | 7.3 | 7.2 | 6.9 | 5.2 | 7.1 | 5.1 | 9.6 | 0.000772 | 0.0007660 | 0.000758 | 0.000216 | 0.000748 | 0.000210 | 0.001348 | 7.8 | 7.7 | 7.5 | 5.3 | 7.6 | 5.2 | 10.6 | 0.000660 | 0.0006560 | 0.000641 | 0.000104 | 0.000639 | 0.000100 | 0.001223 | 494 | 380 | 2 | 6 |
| 6 | 01520 | -243 | -283 | -300 | -473 | -478 | -311 | -486 | -47 | 0.0483057 | 0.1364971 | 0.2367906 | 0.0668984 | 5.9 | 5.8 | 5.7 | 3.8 | 5.8 | 3.7 | 8.3 | 0.000862 | 0.0008600 | 0.000848 | 0.000258 | 0.000840 | 0.000247 | 0.001479 | 6.3 | 6.2 | 6.1 | 3.7 | 6.1 | 3.6 | 9.1 | 0.000660 | 0.0006570 | 0.000676 | 0.000110 | 0.000640 | 0.000105 | 0.001224 | 486 | 360 | 2 | 4 |
| 7 | 01619 | -148 | -169 | -179 | -281 | -290 | -185 | -295 | -25 | 0.0380060 | 0.1095890 | 0.2093933 | 0.0532088 | 5.2 | 5.1 | 5.0 | 3.4 | 5.1 | 3.3 | 7.2 | 0.000900 | 0.0008960 | 0.000894 | 0.000323 | 0.000878 | 0.000312 | 0.001493 | 5.5 | 5.5 | 5.5 | 3.3 | 5.4 | 3.2 | 7.9 | 0.000660 | 0.0006570 | 0.000673 | 0.000109 | 0.000640 | 0.000099 | 0.001219 | 295 | 699 | 3 | 4 |
| 8 | 01744 | -180 | -204 | -215 | -348 | -354 | -223 | -361 | -30 | 0.0338861 | 0.1022505 | 0.1859100 | 0.0433605 | 5.6 | 5.6 | 5.5 | 4.1 | 5.5 | 4.0 | 7.3 | 0.000926 | 0.0009160 | 0.000858 | 0.000433 | 0.000898 | 0.000417 | 0.001462 | 6.0 | 5.9 | 5.9 | 4.3 | 5.9 | 4.2 | 7.9 | 0.000629 | 0.0006220 | 0.000601 | 0.000216 | 0.000608 | 0.000203 | 0.001065 | 361 | 1052 | 3 | 4 |
| 9 | 02107 | -178 | -123 | -106 | -15 | -321 | -112 | -295 | 0 | 0.1265836 | 0.2832681 | 0.4011742 | 0.2280786 | 9.4 | 9.3 | 9.2 | 7.8 | 9.2 | 7.7 | 11.3 | 0.001117 | 0.0011090 | 0.001085 | 0.000783 | 0.001097 | 0.000770 | 0.001480 | 10.3 | 10.2 | 10.0 | 8.5 | 10.1 | 8.4 | 12.4 | 0.000677 | 0.0006730 | 0.000675 | 0.000406 | 0.000664 | 0.000396 | 0.000962 | 355 | 1092 | 4 | 7 |
| 10 | 03466 | -243 | -241 | -239 | -171 | -472 | -250 | -472 | -16 | 0.0132866 | 0.0264188 | 0.0489237 | 0.0076460 | 6.6 | 6.5 | 6.3 | 5.1 | 6.5 | 5.0 | 8.4 | 0.000577 | 0.0005730 | 0.000586 | 0.000138 | 0.000559 | 0.000131 | 0.001032 | 7.1 | 7.0 | 6.8 | 5.4 | 7.0 | 5.3 | 9.1 | 0.000522 | 0.0005230 | 0.000513 | 0.000040 | 0.000508 | 0.000042 | 0.001004 | 486 | 694 | 3 | 4 |
| 11 | 03662 | -194 | -234 | -253 | -375 | -383 | -261 | -389 | -37 | 0.0462458 | 0.1511742 | 0.2954990 | 0.0979728 | 1.5 | 1.5 | 1.5 | -0.1 | 1.4 | -0.1 | 3.1 | 0.001056 | 0.0010510 | 0.001045 | 0.000404 | 0.001030 | 0.000389 | 0.001728 | 1.4 | 1.4 | 1.4 | -0.2 | 1.3 | -0.2 | 3.1 | 0.000721 | 0.0007150 | 0.000731 | 0.000195 | 0.000698 | 0.000182 | 0.001273 | 389 | 2116 | 4 | 4 |
| 12 | 0433A | -237 | -263 | -274 | -460 | -465 | -285 | -473 | -37 | 0.0318261 | 0.0924658 | 0.1545988 | 0.0335181 | 5.7 | 5.7 | 5.5 | 4.0 | 5.6 | 3.9 | 7.7 | 0.000883 | 0.0008680 | 0.000830 | 0.000458 | 0.000853 | 0.000431 | 0.001361 | 6.0 | 6.0 | 5.8 | 4.1 | 5.9 | 4.0 | 8.2 | 0.000704 | 0.0007000 | 0.000694 | 0.000173 | 0.000683 | 0.000159 | 0.001245 | 474 | 1043 | 3 | 3 |
| 13 | 04471 | -271 | -351 | -376 | -523 | -534 | -386 | -542 | -87 | 0.1162839 | 0.2685910 | 0.3855186 | 0.2054997 | 5.6 | 5.6 | 5.5 | 4.0 | 5.5 | 3.9 | 7.5 | 0.001024 | 0.0010110 | 0.001001 | 0.000517 | 0.000994 | 0.000496 | 0.001574 | 6.0 | 6.0 | 5.9 | 4.3 | 5.9 | 4.2 | 8.0 | 0.000445 | 0.0004390 | 0.000422 | 0.000041 | 0.000426 | 0.000029 | 0.000870 | 542 | 0 | 1 | 2 |
| 14 | 04703 | -84 | -124 | -137 | -160 | -168 | -140 | -169 | -34 | 0.1574827 | 0.4129159 | 0.6007828 | 0.5252067 | 7.3 | 7.3 | 7.4 | 5.5 | 7.3 | 5.5 | 9.1 | 0.001262 | 0.0012530 | 0.001222 | 0.000518 | 0.001229 | 0.000501 | 0.002031 | 8.5 | 8.5 | 8.6 | 6.6 | 8.5 | 6.6 | 10.5 | 0.000395 | 0.0004050 | 0.000411 | -0.000229 | 0.000387 | -0.000209 | 0.000979 | 169 | 2533 | 8 | 7 |
| 15 | 04953 | -141 | -119 | -111 | -16 | -269 | -117 | -256 | 0 | 0.0462458 | 0.1316047 | 0.2172211 | 0.0608924 | 8.2 | 8.2 | 8.1 | 6.7 | 8.1 | 6.6 | 10.0 | 0.001093 | 0.0010840 | 0.001069 | 0.000803 | 0.001073 | 0.000786 | 0.001419 | 8.9 | 8.9 | 8.8 | 7.2 | 8.8 | 7.1 | 10.8 | 0.000746 | 0.0007410 | 0.000718 | 0.000432 | 0.000730 | 0.000421 | 0.001077 | 282 | 1359 | 5 | 5 |
| 16 | 05735 | -112 | -121 | -125 | -205 | -218 | -131 | -221 | -15 | 0.0277063 | 0.0680039 | 0.1272016 | 0.0243367 | 5.8 | 5.8 | 5.8 | 4.5 | 5.8 | 4.5 | 7.2 | 0.001087 | 0.0010770 | 0.001060 | 0.000654 | 0.001062 | 0.000634 | 0.001555 | 6.2 | 6.2 | 6.2 | 4.8 | 6.2 | 4.8 | 7.8 | 0.000534 | 0.0005300 | 0.000533 | 0.000185 | 0.000519 | 0.000177 | 0.000898 | 223 | 2285 | 4 | 7 |
| 17 | 05824 | -72 | -69 | -67 | -15 | -140 | -71 | -137 | -3 | 0.0235864 | 0.0459883 | 0.0802348 | 0.0145830 | 8.4 | 8.4 | 8.4 | 7.1 | 8.4 | 7.1 | 9.8 | 0.000725 | 0.0007160 | 0.000707 | 0.000455 | 0.000707 | 0.000437 | 0.001027 | 9.2 | 9.2 | 9.2 | 7.7 | 9.1 | 7.6 | 10.8 | 0.000558 | 0.0005530 | 0.000529 | 0.000180 | 0.000541 | 0.000169 | 0.000952 | 144 | 1825 | 4 | 7 |
| 18 | 06263 | -253 | -182 | -160 | -17 | -463 | -169 | -428 | 0 | 0.1101040 | 0.2588063 | 0.3659491 | 0.1885405 | 8.5 | 8.4 | 8.2 | 6.7 | 8.3 | 6.6 | 10.5 | 0.000934 | 0.0009190 | 0.000898 | 0.000618 | 0.000908 | 0.000595 | 0.001303 | 9.2 | 9.1 | 8.9 | 7.1 | 9.0 | 7.0 | 11.5 | 0.000635 | 0.0006310 | 0.000621 | 0.000106 | 0.000615 | 0.000103 | 0.001174 | 506 | 406 | 2 | 5 |
| 19 | 06436 | -228 | -248 | -257 | -394 | -445 | -268 | -453 | -34 | 0.0297662 | 0.0777886 | 0.1311155 | 0.0256822 | 6.5 | 6.4 | 6.2 | 4.4 | 6.3 | 4.3 | 8.8 | 0.000866 | 0.0008630 | 0.000880 | 0.000283 | 0.000844 | 0.000273 | 0.001464 | 6.9 | 6.8 | 6.6 | 4.4 | 6.7 | 4.3 | 9.7 | 0.000659 | 0.0006560 | 0.000623 | 0.000107 | 0.000639 | 0.000106 | 0.001223 | 455 | 1060 | 4 | 3 |
| 20 | 06441 | -211 | -251 | -269 | -409 | -416 | -279 | -422 | -40 | 0.0462458 | 0.1389432 | 0.2759295 | 0.0853471 | 4.6 | 4.5 | 4.4 | 3.1 | 4.5 | 3.0 | 6.3 | 0.000712 | 0.0007050 | 0.000706 | 0.000242 | 0.000690 | 0.000230 | 0.001208 | 4.6 | 4.5 | 4.3 | 2.8 | 4.5 | 2.7 | 6.6 | 0.000873 | 0.0008650 | 0.000858 | 0.000387 | 0.000849 | 0.000373 | 0.001391 | 422 | 351 | 2 | 5 |
| 21 | 06978 | -240 | -284 | -301 | -453 | -472 | -311 | -481 | -50 | 0.0565455 | 0.1585127 | 0.2485323 | 0.0811291 | 6.1 | 6.0 | 5.8 | 3.8 | 5.9 | 3.7 | 8.7 | 0.001093 | 0.0010880 | 0.001045 | 0.000487 | 0.001068 | 0.000479 | 0.001724 | 6.5 | 6.4 | 6.1 | 3.7 | 6.3 | 3.6 | 9.5 | 0.000660 | 0.0006560 | 0.000653 | 0.000110 | 0.000639 | 0.000106 | 0.001231 | 481 | 736 | 3 | 8 |
| 22 | 07127 | -195 | -143 | -123 | -13 | -362 | -131 | -339 | 0 | 0.0853847 | 0.2270059 | 0.3659491 | 0.1662341 | 8.4 | 8.3 | 8.2 | 6.8 | 8.2 | 6.7 | 10.1 | 0.000831 | 0.0008230 | 0.000817 | 0.000436 | 0.000810 | 0.000422 | 0.001254 | 8.9 | 8.9 | 8.8 | 7.2 | 8.8 | 7.1 | 10.9 | 0.000641 | 0.0006370 | 0.000631 | 0.000204 | 0.000623 | 0.000194 | 0.001087 | 390 | 2184 | 5 | 3 |
| 23 | 07478 | -364 | -444 | -468 | -605 | -716 | -482 | -729 | -100 | 0.0915645 | 0.2123288 | 0.2837573 | 0.1278200 | 6.0 | 5.9 | 5.8 | 3.8 | 5.8 | 3.6 | 8.5 | 0.001105 | 0.0010910 | 0.001054 | 0.000671 | 0.001076 | 0.000646 | 0.001587 | 6.4 | 6.3 | 5.9 | 3.9 | 6.2 | 3.7 | 9.2 | 0.000694 | 0.0006900 | 0.000697 | 0.000184 | 0.000674 | 0.000180 | 0.001220 | 729 | 578 | 2 | 6 |
| 24 | 07516 | -311 | -241 | -217 | -58 | -580 | -229 | -543 | 0 | 0.0812648 | 0.2074364 | 0.2994129 | 0.1266943 | 7.8 | 7.7 | 7.5 | 6.0 | 7.6 | 5.8 | 9.9 | 0.000764 | 0.0007540 | 0.000745 | 0.000308 | 0.000739 | 0.000287 | 0.001257 | 8.4 | 8.2 | 7.9 | 6.3 | 8.2 | 6.2 | 10.8 | 0.000699 | 0.0006920 | 0.000677 | 0.000291 | 0.000679 | 0.000282 | 0.001134 | 622 | 365 | 2 | 5 |
| 25 | 07620 | -255 | -276 | -285 | -323 | -498 | -296 | -507 | -36 | 0.0277063 | 0.0802348 | 0.1193738 | 0.0243686 | 6.5 | 6.4 | 6.2 | 4.4 | 6.4 | 4.3 | 9.0 | 0.000875 | 0.0008720 | 0.000872 | 0.000292 | 0.000853 | 0.000286 | 0.001470 | 7.0 | 6.9 | 6.7 | 4.4 | 6.8 | 4.3 | 9.9 | 0.000659 | 0.0006560 | 0.000664 | 0.000108 | 0.000639 | 0.000103 | 0.001226 | 510 | 351 | 2 | 9 |
| 26 | 07906 | -64 | -60 | -58 | -21 | -125 | -61 | -121 | -2 | 0.0297662 | 0.0631115 | 0.1154599 | 0.0222483 | 8.2 | 8.2 | 8.3 | 6.5 | 8.2 | 6.5 | 10.1 | 0.001158 | 0.0011520 | 0.001142 | 0.000583 | 0.001133 | 0.000568 | 0.001758 | 8.9 | 8.9 | 8.8 | 6.7 | 8.8 | 6.7 | 11.1 | 0.000661 | 0.0006570 | 0.000649 | 0.000109 | 0.000641 | 0.000101 | 0.001217 | 129 | 733 | 3 | 8 |
| 27 | 11709 | -76 | -72 | -69 | -15 | -148 | -73 | -144 | -2 | 0.0256463 | 0.0582192 | 0.1037182 | 0.0189306 | 8.4 | 8.4 | 8.3 | 6.8 | 8.3 | 6.7 | 10.1 | 0.000813 | 0.0008080 | 0.000792 | 0.000385 | 0.000795 | 0.000378 | 0.001260 | 9.0 | 9.0 | 9.0 | 7.0 | 8.9 | 6.9 | 11.2 | 0.000660 | 0.0006550 | 0.000652 | 0.000109 | 0.000638 | 0.000107 | 0.001227 | 153 | 735 | 3 | 7 |
| 28 | 11726 | -77 | -75 | -74 | -14 | -150 | -78 | -147 | -4 | 0.0235864 | 0.0386497 | 0.0528376 | 0.0117253 | 7.8 | 7.7 | 7.7 | 6.2 | 7.7 | 6.1 | 9.4 | 0.000735 | 0.0007320 | 0.000740 | 0.000297 | 0.000718 | 0.000291 | 0.001188 | 8.4 | 8.3 | 8.3 | 6.3 | 8.3 | 6.3 | 10.5 | 0.000659 | 0.0006560 | 0.000633 | 0.000110 | 0.000639 | 0.000107 | 0.001224 | 154 | 2559 | 6 | 4 |
| 29 | 12672 | -179 | -187 | -190 | -303 | -350 | -199 | -354 | -18 | 0.0132866 | 0.0386497 | 0.0684932 | 0.0105575 | 6.2 | 6.2 | 6.1 | 4.9 | 6.1 | 4.8 | 7.7 | 0.000618 | 0.0006070 | 0.000582 | 0.000370 | 0.000599 | 0.000353 | 0.000906 | 6.5 | 6.5 | 6.5 | 4.9 | 6.4 | 4.9 | 8.3 | 0.000623 | 0.0006210 | 0.000626 | 0.000135 | 0.000606 | 0.000127 | 0.001114 | 358 | 1172 | 3 | 5 |
| 30 | 13062 | -192 | -194 | -195 | -246 | -375 | -205 | -376 | -14 | 0.0091668 | 0.0166341 | 0.0254403 | 0.0061715 | 6.0 | 6.0 | 6.0 | 4.8 | 5.9 | 4.7 | 7.4 | 0.000443 | 0.0004290 | 0.000402 | 0.000055 | 0.000416 | 0.000026 | 0.000878 | 6.3 | 6.3 | 6.2 | 4.9 | 6.2 | 4.8 | 7.9 | 0.000374 | 0.0003650 | 0.000335 | -0.000008 | 0.000352 | -0.000022 | 0.000785 | 385 | 536 | 3 | 3 |
| 31 | 13737 | -330 | -367 | -380 | -484 | -645 | -394 | -658 | -58 | 0.0421259 | 0.1095890 | 0.1545988 | 0.0418443 | 6.1 | 6.0 | 5.9 | 4.3 | 6.0 | 4.2 | 8.3 | 0.000801 | 0.0007970 | 0.000800 | 0.000301 | 0.000780 | 0.000293 | 0.001323 | 6.5 | 6.4 | 6.2 | 4.5 | 6.3 | 4.3 | 8.9 | 0.000628 | 0.0006260 | 0.000616 | 0.000134 | 0.000611 | 0.000134 | 0.001129 | 659 | 435 | 2 | 7 |
| 32 | 14353 | -310 | -454 | -495 | -609 | -616 | -504 | -621 | -145 | 0.1925018 | 0.4300391 | 0.5890411 | 0.5000178 | 3.4 | 3.4 | 3.1 | 1.3 | 3.3 | 1.2 | 5.6 | 0.001230 | 0.0012200 | 0.001160 | 0.000634 | 0.001200 | 0.000620 | 0.001867 | 3.6 | 3.5 | 3.3 | 1.5 | 3.4 | 1.3 | 5.9 | 0.000600 | 0.0005940 | 0.000577 | 0.000132 | 0.000579 | 0.000123 | 0.001091 | 621 | 356 | 2 | 8 |
| 33 | 14732 | -290 | -192 | -164 | -16 | -512 | -174 | -466 | 0 | 0.1533629 | 0.3248532 | 0.4324853 | 0.2786456 | 8.1 | 8.0 | 7.8 | 6.3 | 8.0 | 6.2 | 10.3 | 0.001034 | 0.0010230 | 0.001007 | 0.000530 | 0.001006 | 0.000508 | 0.001583 | 8.8 | 8.7 | 8.5 | 6.8 | 8.6 | 6.6 | 11.3 | 0.000780 | 0.0007730 | 0.000779 | 0.000349 | 0.000758 | 0.000332 | 0.001239 | 580 | 1113 | 3 | 7 |
| 34 | 14A0D | -76 | -74 | -73 | -24 | -149 | -77 | -146 | -4 | 0.0235864 | 0.0386497 | 0.0684932 | 0.0125687 | 7.7 | 7.6 | 7.5 | 5.9 | 7.6 | 5.9 | 9.5 | 0.001080 | 0.0010740 | 0.001069 | 0.000498 | 0.001056 | 0.000485 | 0.001683 | 8.2 | 8.2 | 8.0 | 6.1 | 8.1 | 6.0 | 10.5 | 0.000660 | 0.0006580 | 0.000669 | 0.000110 | 0.000641 | 0.000106 | 0.001220 | 153 | 1949 | 7 | 8 |
| 35 | 15627 | -69 | -70 | -71 | -92 | -135 | -74 | -134 | -6 | 0.0256463 | 0.0410959 | 0.0567515 | 0.0121930 | 7.9 | 7.8 | 7.8 | 6.5 | 7.8 | 6.5 | 9.3 | 0.001117 | 0.0011060 | 0.001097 | 0.000677 | 0.001092 | 0.000659 | 0.001598 | 8.6 | 8.6 | 8.5 | 7.1 | 8.5 | 7.0 | 10.2 | 0.000558 | 0.0005520 | 0.000539 | 0.000226 | 0.000541 | 0.000214 | 0.000916 | 138 | 756 | 3 | 4 |
| 36 | 15660 | -238 | -192 | -172 | -22 | -451 | -182 | -428 | 0 | 0.0524256 | 0.1511742 | 0.2759295 | 0.0884005 | 9.3 | 9.2 | 9.1 | 8.0 | 9.2 | 7.9 | 10.7 | 0.000338 | 0.0003360 | 0.000331 | 0.000192 | 0.000332 | 0.000188 | 0.000491 | 10.1 | 10.1 | 10.0 | 8.7 | 10.0 | 8.6 | 11.8 | 0.000281 | 0.0002800 | 0.000283 | 0.000110 | 0.000274 | 0.000108 | 0.000459 | 475 | 90 | 2 | 9 |
| 37 | 15690 | -246 | -288 | -304 | -463 | -483 | -315 | -492 | -47 | 0.0483057 | 0.1389432 | 0.2328767 | 0.0715822 | 6.0 | 5.9 | 5.6 | 3.8 | 5.8 | 3.7 | 8.4 | 0.000935 | 0.0009300 | 0.000916 | 0.000386 | 0.000912 | 0.000371 | 0.001493 | 6.4 | 6.3 | 6.1 | 3.8 | 6.2 | 3.6 | 9.3 | 0.000660 | 0.0006570 | 0.000614 | 0.000109 | 0.000639 | 0.000103 | 0.001222 | 492 | 1462 | 5 | 9 |
| 38 | 15718 | -162 | -116 | -98 | -11 | -302 | -105 | -282 | 0 | 0.0833248 | 0.2367906 | 0.3855186 | 0.1833299 | 9.6 | 9.6 | 9.5 | 8.0 | 9.5 | 7.9 | 11.4 | 0.000736 | 0.0007330 | 0.000704 | 0.000064 | 0.000711 | 0.000050 | 0.001415 | 10.4 | 10.3 | 10.2 | 8.3 | 10.3 | 8.2 | 12.7 | 0.000660 | 0.0006570 | 0.000654 | 0.000108 | 0.000640 | 0.000103 | 0.001224 | 324 | 1477 | 5 | 8 |
| 39 | 15738 | -182 | -112 | -91 | -12 | -320 | -97 | -291 | 0 | 0.1616026 | 0.3517613 | 0.4951076 | 0.3462025 | 9.5 | 9.5 | 9.3 | 8.0 | 9.4 | 7.9 | 11.3 | 0.000856 | 0.0008490 | 0.000836 | 0.000582 | 0.000840 | 0.000568 | 0.001155 | 10.3 | 10.2 | 10.1 | 8.5 | 10.2 | 8.4 | 12.3 | 0.000663 | 0.0006590 | 0.000640 | 0.000127 | 0.000643 | 0.000124 | 0.001211 | 365 | 1903 | 4 | 4 |
| 40 | 15748 | -176 | -147 | -135 | -16 | -335 | -143 | -318 | 0 | 0.0462458 | 0.1291585 | 0.2328767 | 0.0646188 | 8.7 | 8.6 | 8.5 | 6.9 | 8.6 | 6.8 | 10.6 | 0.000671 | 0.0006660 | 0.000669 | 0.000379 | 0.000656 | 0.000370 | 0.000977 | 9.4 | 9.3 | 9.1 | 7.1 | 9.2 | 7.0 | 11.8 | 0.000660 | 0.0006570 | 0.000665 | 0.000113 | 0.000640 | 0.000106 | 0.001224 | 351 | 1482 | 6 | 6 |
| 41 | 15755 | -243 | -257 | -264 | -471 | -475 | -276 | -483 | -26 | 0.0112267 | 0.0410959 | 0.0880626 | 0.0127823 | 5.8 | 5.8 | 5.6 | 4.3 | 5.7 | 4.2 | 7.6 | 0.000306 | 0.0002950 | 0.000272 | 0.000006 | 0.000285 | -0.000012 | 0.000646 | 6.2 | 6.2 | 6.0 | 4.2 | 6.1 | 4.1 | 8.4 | 0.000660 | 0.0006570 | 0.000663 | 0.000111 | 0.000640 | 0.000108 | 0.001223 | 486 | 763 | 3 | 3 |
| 42 | 15765 | -236 | -215 | -206 | -120 | -453 | -217 | -438 | -2 | 0.0256463 | 0.0777886 | 0.1272016 | 0.0246630 | 7.8 | 7.7 | 7.5 | 5.9 | 7.6 | 5.8 | 10.0 | 0.000739 | 0.0007330 | 0.000727 | 0.000445 | 0.000724 | 0.000433 | 0.001055 | 8.3 | 8.3 | 8.2 | 6.0 | 8.2 | 5.9 | 11.0 | 0.000659 | 0.0006550 | 0.000637 | 0.000112 | 0.000638 | 0.000106 | 0.001220 | 471 | 1419 | 4 | 4 |
| 43 | 15781 | -362 | -479 | -514 | -700 | -715 | -527 | -724 | -119 | 0.1204037 | 0.2979452 | 0.4207436 | 0.2450812 | 5.5 | 5.5 | 5.4 | 2.9 | 5.4 | 2.8 | 8.5 | 0.001014 | 0.0010100 | 0.000993 | 0.000450 | 0.000992 | 0.000436 | 0.001584 | 5.9 | 5.8 | 5.6 | 2.8 | 5.7 | 2.6 | 9.3 | 0.000659 | 0.0006550 | 0.000644 | 0.000108 | 0.000638 | 0.000105 | 0.001221 | 724 | 831 | 3 | 7 |
| 44 | 15803 | -327 | -388 | -405 | -488 | -640 | -417 | -654 | -87 | 0.0874446 | 0.1927593 | 0.2485323 | 0.1090602 | 6.6 | 6.5 | 6.5 | 4.2 | 6.4 | 4.0 | 9.2 | 0.001287 | 0.0012750 | 0.001278 | 0.000819 | 0.001259 | 0.000796 | 0.001799 | 7.1 | 7.0 | 6.8 | 4.4 | 6.9 | 4.3 | 10.0 | 0.000917 | 0.0009080 | 0.000885 | 0.000543 | 0.000895 | 0.000523 | 0.001328 | 654 | 381 | 2 | 5 |
| 45 | 15816 | -182 | -153 | -142 | -38 | -346 | -150 | -328 | 0 | 0.0503656 | 0.1413894 | 0.2250489 | 0.0658075 | 8.2 | 8.2 | 8.1 | 6.5 | 8.1 | 6.5 | 10.1 | 0.001038 | 0.0010270 | 0.001011 | 0.000619 | 0.001013 | 0.000600 | 0.001490 | 8.9 | 8.9 | 8.7 | 7.0 | 8.8 | 6.9 | 11.1 | 0.000636 | 0.0006330 | 0.000639 | 0.000098 | 0.000617 | 0.000090 | 0.001181 | 364 | 1836 | 2 | 8 |
| 46 | 16031 | -232 | -247 | -251 | -274 | -453 | -261 | -461 | -34 | 0.0318261 | 0.0826810 | 0.1232877 | 0.0249342 | 7.2 | 7.2 | 7.1 | 5.3 | 7.1 | 5.2 | 9.5 | 0.001013 | 0.0010060 | 0.001000 | 0.000469 | 0.000988 | 0.000453 | 0.001580 | 7.9 | 7.8 | 7.6 | 5.6 | 7.7 | 5.5 | 10.4 | 0.000519 | 0.0005190 | 0.000523 | 0.000061 | 0.000504 | 0.000062 | 0.000978 | 465 | 463 | 2 | 4 |
| 47 | 16039 | -250 | -214 | -198 | -17 | -478 | -210 | -458 | 0 | 0.0338861 | 0.1046967 | 0.2054795 | 0.0497771 | 8.8 | 8.8 | 8.6 | 6.9 | 8.7 | 6.9 | 10.8 | 0.000357 | 0.0003470 | 0.000343 | 0.000125 | 0.000339 | 0.000106 | 0.000624 | 9.5 | 9.5 | 9.5 | 7.2 | 9.4 | 7.1 | 11.9 | 0.000658 | 0.0006550 | 0.000656 | 0.000110 | 0.000638 | 0.000102 | 0.001216 | 499 | 1806 | 6 | 4 |
| 48 | 16042 | -176 | -206 | -219 | -338 | -346 | -226 | -352 | -32 | 0.0441858 | 0.1340509 | 0.2407045 | 0.0685383 | 5.4 | 5.3 | 5.2 | 3.4 | 5.3 | 3.4 | 7.6 | 0.000946 | 0.0009410 | 0.000930 | 0.000336 | 0.000922 | 0.000331 | 0.001575 | 5.7 | 5.6 | 5.5 | 3.3 | 5.6 | 3.2 | 8.4 | 0.000659 | 0.0006560 | 0.000666 | 0.000110 | 0.000638 | 0.000105 | 0.001221 | 352 | 393 | 2 | 5 |
| 49 | 16044 | -114 | -122 | -126 | -203 | -222 | -132 | -225 | -14 | 0.0235864 | 0.0631115 | 0.1115460 | 0.0202327 | 5.3 | 5.3 | 5.3 | 4.1 | 5.3 | 4.0 | 6.7 | 0.000877 | 0.0008680 | 0.000834 | 0.000452 | 0.000854 | 0.000437 | 0.001334 | 5.6 | 5.6 | 5.6 | 4.2 | 5.6 | 4.2 | 7.2 | 0.000513 | 0.0005130 | 0.000516 | 0.000108 | 0.000500 | 0.000106 | 0.000922 | 227 | 390 | 2 | 2 |
| 50 | 16055 | -318 | -351 | -361 | -479 | -621 | -374 | -635 | -57 | 0.0441858 | 0.1120352 | 0.1545988 | 0.0423542 | 6.6 | 6.5 | 6.3 | 4.2 | 6.4 | 4.1 | 9.4 | 0.000960 | 0.0009520 | 0.000936 | 0.000392 | 0.000934 | 0.000375 | 0.001547 | 7.1 | 7.0 | 6.7 | 4.2 | 6.9 | 4.1 | 10.3 | 0.000659 | 0.0006560 | 0.000661 | 0.000108 | 0.000639 | 0.000101 | 0.001221 | 636 | 1482 | 3 | 6 |
| 51 | 16072 | -240 | -306 | -331 | -465 | -474 | -340 | -480 | -63 | 0.0853847 | 0.2343444 | 0.3737769 | 0.1747013 | 4.8 | 4.7 | 4.5 | 2.9 | 4.6 | 2.8 | 7.0 | 0.001003 | 0.0009970 | 0.000976 | 0.000378 | 0.000978 | 0.000369 | 0.001638 | 5.1 | 5.0 | 4.7 | 2.8 | 4.9 | 2.7 | 7.5 | 0.000647 | 0.0006430 | 0.000638 | 0.000151 | 0.000627 | 0.000140 | 0.001155 | 480 | 1082 | 3 | 3 |
| 52 | 16076 | -290 | -246 | -228 | -69 | -552 | -241 | -524 | 0 | 0.0503656 | 0.1389432 | 0.2133072 | 0.0642401 | 7.8 | 7.7 | 7.4 | 6.0 | 7.6 | 5.9 | 9.8 | 0.000701 | 0.0006880 | 0.000641 | 0.000371 | 0.000677 | 0.000348 | 0.001077 | 8.4 | 8.3 | 8.3 | 6.4 | 8.2 | 6.2 | 10.8 | 0.000633 | 0.0006290 | 0.000604 | 0.000117 | 0.000612 | 0.000107 | 0.001162 | 581 | 345 | 2 | 2 |
| 53 | 16081 | -234 | -292 | -314 | -456 | -462 | -323 | -468 | -58 | 0.0771449 | 0.2123288 | 0.3385519 | 0.1441829 | 5.4 | 5.3 | 5.2 | 3.3 | 5.2 | 3.2 | 7.8 | 0.001061 | 0.0010520 | 0.001049 | 0.000507 | 0.001033 | 0.000494 | 0.001650 | 5.7 | 5.7 | 5.5 | 3.2 | 5.6 | 3.0 | 8.6 | 0.000658 | 0.0006550 | 0.000641 | 0.000112 | 0.000638 | 0.000107 | 0.001217 | 468 | 729 | 3 | 7 |
| 54 | 16082 | -292 | -378 | -408 | -570 | -578 | -419 | -585 | -82 | 0.0956844 | 0.2514677 | 0.3933464 | 0.1951994 | 4.7 | 4.6 | 4.5 | 2.8 | 4.6 | 2.7 | 6.8 | 0.000968 | 0.0009640 | 0.000971 | 0.000366 | 0.000945 | 0.000358 | 0.001590 | 4.9 | 4.8 | 4.5 | 2.8 | 4.7 | 2.6 | 7.3 | 0.000696 | 0.0006920 | 0.000697 | 0.000170 | 0.000675 | 0.000168 | 0.001245 | 585 | 343 | 2 | 6 |
| 55 | 16083 | -140 | -134 | -131 | -83 | -272 | -138 | -265 | -5 | 0.0215264 | 0.0459883 | 0.0724070 | 0.0117334 | 7.5 | 7.5 | 7.4 | 5.7 | 7.4 | 5.6 | 9.6 | 0.000968 | 0.0009630 | 0.000954 | 0.000489 | 0.000947 | 0.000480 | 0.001472 | 8.1 | 8.0 | 8.0 | 5.8 | 8.0 | 5.7 | 10.6 | 0.000659 | 0.0006550 | 0.000636 | 0.000107 | 0.000639 | 0.000100 | 0.001218 | 280 | 615 | 2 | 2 |
| 56 | 16103 | -293 | -232 | -209 | -15 | -552 | -221 | -521 | 0 | 0.0647853 | 0.1829746 | 0.2876712 | 0.1049415 | 7.9 | 7.8 | 7.7 | 6.3 | 7.8 | 6.1 | 10.0 | 0.000609 | 0.0006030 | 0.000602 | 0.000098 | 0.000586 | 0.000087 | 0.001137 | 8.6 | 8.5 | 8.3 | 6.6 | 8.4 | 6.5 | 10.9 | 0.000645 | 0.0006410 | 0.000644 | 0.000125 | 0.000626 | 0.000120 | 0.001184 | 586 | 756 | 4 | 4 |
| 57 | 16327 | -290 | -382 | -415 | -566 | -573 | -425 | -580 | -91 | 0.1121640 | 0.2783757 | 0.4285714 | 0.2347622 | 4.8 | 4.7 | 4.7 | 2.9 | 4.7 | 2.8 | 6.9 | 0.001050 | 0.0010430 | 0.001033 | 0.000479 | 0.001025 | 0.000468 | 0.001651 | 5.0 | 5.0 | 4.7 | 3.0 | 4.9 | 2.9 | 7.3 | 0.000615 | 0.0006130 | 0.000613 | 0.000130 | 0.000597 | 0.000127 | 0.001115 | 580 | 741 | 3 | 8 |
| 58 | 16329 | -246 | -276 | -288 | -450 | -483 | -299 | -492 | -41 | 0.0359460 | 0.1022505 | 0.1663405 | 0.0391324 | 6.0 | 5.9 | 5.7 | 4.3 | 5.9 | 4.2 | 8.1 | 0.000809 | 0.0008030 | 0.000799 | 0.000311 | 0.000787 | 0.000301 | 0.001329 | 6.4 | 6.3 | 6.2 | 4.4 | 6.3 | 4.3 | 8.8 | 0.000649 | 0.0006450 | 0.000646 | 0.000177 | 0.000630 | 0.000168 | 0.001134 | 493 | 557 | 3 | 3 |
| 59 | 16330 | -196 | -164 | -151 | -44 | -372 | -160 | -351 | 0 | 0.0565455 | 0.1487280 | 0.2328767 | 0.0717558 | 7.9 | 7.8 | 7.6 | 5.8 | 7.7 | 5.7 | 10.3 | 0.001139 | 0.0011290 | 0.001095 | 0.000637 | 0.001112 | 0.000617 | 0.001676 | 8.5 | 8.4 | 8.3 | 6.0 | 8.3 | 5.9 | 11.3 | 0.000660 | 0.0006580 | 0.000672 | 0.000109 | 0.000641 | 0.000105 | 0.001227 | 392 | 380 | 2 | 5 |
| 60 | 16343 | -152 | -189 | -205 | -291 | -299 | -211 | -303 | -34 | 0.0647853 | 0.1903131 | 0.3463796 | 0.1390078 | 3.6 | 3.5 | 3.4 | 1.8 | 3.5 | 1.7 | 5.7 | 0.001184 | 0.0011770 | 0.001194 | 0.000530 | 0.001156 | 0.000517 | 0.001864 | 3.7 | 3.7 | 3.6 | 1.4 | 3.6 | 1.3 | 6.2 | 0.000659 | 0.0006560 | 0.000649 | 0.000112 | 0.000639 | 0.000109 | 0.001223 | 303 | 648 | 2 | 3 |
| 61 | 16345 | -178 | -196 | -204 | -300 | -350 | -212 | -355 | -24 | 0.0235864 | 0.0753425 | 0.1467710 | 0.0272248 | 6.2 | 6.2 | 6.0 | 4.3 | 6.1 | 4.2 | 8.4 | 0.000887 | 0.0008840 | 0.000876 | 0.000284 | 0.000865 | 0.000277 | 0.001504 | 6.6 | 6.6 | 6.4 | 4.3 | 6.5 | 4.2 | 9.2 | 0.000659 | 0.0006560 | 0.000674 | 0.000112 | 0.000639 | 0.000108 | 0.001222 | 357 | 485 | 2 | 9 |
| 62 | 16368 | -320 | -416 | -450 | -625 | -631 | -461 | -639 | -98 | 0.1080441 | 0.2710372 | 0.4050881 | 0.2124623 | 5.4 | 5.3 | 5.1 | 2.9 | 5.2 | 2.8 | 8.2 | 0.001007 | 0.0010010 | 0.000991 | 0.000421 | 0.000982 | 0.000407 | 0.001614 | 5.7 | 5.6 | 5.5 | 2.7 | 5.5 | 2.6 | 9.0 | 0.000661 | 0.0006580 | 0.000671 | 0.000109 | 0.000641 | 0.000102 | 0.001221 | 639 | 1151 | 3 | 5 |
| 63 | 16377 | -226 | -138 | -116 | -35 | -385 | -123 | -343 | 0 | 0.2028015 | 0.3786693 | 0.4872798 | 0.3716367 | 8.3 | 8.2 | 8.0 | 6.1 | 8.1 | 5.9 | 10.9 | 0.001529 | 0.0015180 | 0.001491 | 0.000942 | 0.001498 | 0.000918 | 0.002156 | 9.0 | 8.9 | 8.7 | 6.3 | 8.8 | 6.2 | 12.0 | 0.000661 | 0.0006560 | 0.000640 | 0.000112 | 0.000639 | 0.000106 | 0.001225 | 453 | 702 | 3 | 6 |
| 64 | 16381 | -274 | -264 | -260 | -163 | -530 | -273 | -520 | -8 | 0.0153466 | 0.0459883 | 0.0724070 | 0.0118928 | 7.0 | 6.9 | 6.7 | 5.3 | 6.9 | 5.2 | 9.0 | 0.000572 | 0.0005620 | 0.000556 | 0.000207 | 0.000550 | 0.000189 | 0.000970 | 7.5 | 7.4 | 7.3 | 5.6 | 7.4 | 5.5 | 9.8 | 0.000647 | 0.0006440 | 0.000627 | 0.000125 | 0.000628 | 0.000121 | 0.001171 | 548 | 739 | 3 | 6 |
| 65 | 16384 | -150 | -152 | -152 | -209 | -292 | -160 | -292 | -12 | 0.0153466 | 0.0239726 | 0.0332681 | 0.0074737 | 6.9 | 6.9 | 6.8 | 5.2 | 6.8 | 5.1 | 8.8 | 0.000632 | 0.0006290 | 0.000647 | 0.000151 | 0.000614 | 0.000143 | 0.001120 | 7.4 | 7.4 | 7.3 | 5.3 | 7.3 | 5.2 | 9.8 | 0.000660 | 0.0006570 | 0.000668 | 0.000109 | 0.000640 | 0.000105 | 0.001224 | 299 | 396 | 2 | 9 |
| 66 | 16385 | -252 | -264 | -272 | -492 | -495 | -286 | -501 | -21 | 0.0009270 | 0.0092955 | 0.0763209 | 0.0146367 | 5.3 | 5.2 | 5.0 | 3.6 | 5.2 | 3.5 | 7.4 | 0.000952 | 0.0009390 | 0.000912 | 0.000441 | 0.000922 | 0.000417 | 0.001507 | 5.3 | 5.2 | 5.0 | 3.1 | 5.1 | 3.0 | 7.8 | 0.000838 | 0.0008280 | 0.000815 | 0.000268 | 0.000810 | 0.000250 | 0.001443 | 505 | 740 | 3 | 9 |
| 67 | 16397 | -175 | -129 | -111 | -12 | -327 | -118 | -306 | 0 | 0.0792049 | 0.2221135 | 0.3581213 | 0.1593881 | 9.1 | 9.0 | 8.9 | 7.2 | 8.9 | 7.1 | 11.1 | 0.000812 | 0.0008070 | 0.000801 | 0.000283 | 0.000790 | 0.000276 | 0.001363 | 9.8 | 9.7 | 9.8 | 7.5 | 9.7 | 7.4 | 12.3 | 0.000659 | 0.0006550 | 0.000657 | 0.000112 | 0.000638 | 0.000109 | 0.001228 | 350 | 392 | 2 | 9 |
| 68 | 16418 | -196 | -155 | -138 | -13 | -371 | -147 | -351 | 0 | 0.0586054 | 0.1682975 | 0.2915851 | 0.1019815 | 8.8 | 8.7 | 8.6 | 7.0 | 8.7 | 6.9 | 10.9 | 0.000689 | 0.0006820 | 0.000660 | 0.000270 | 0.000669 | 0.000259 | 0.001131 | 9.5 | 9.4 | 9.4 | 7.2 | 9.4 | 7.1 | 12.0 | 0.000659 | 0.0006560 | 0.000658 | 0.000108 | 0.000640 | 0.000102 | 0.001220 | 392 | 1130 | 2 | 5 |
| 69 | 16424 | -175 | -126 | -108 | -16 | -324 | -115 | -301 | 0 | 0.0956844 | 0.2441292 | 0.3776908 | 0.1826703 | 8.7 | 8.6 | 8.6 | 6.8 | 8.6 | 6.6 | 10.9 | 0.001090 | 0.0010820 | 0.001073 | 0.000539 | 0.001064 | 0.000522 | 0.001673 | 9.4 | 9.3 | 9.1 | 7.0 | 9.2 | 6.9 | 12.0 | 0.000659 | 0.0006560 | 0.000650 | 0.000109 | 0.000640 | 0.000100 | 0.001217 | 350 | 1072 | 4 | 3 |
| 70 | 16431 | -177 | -200 | -210 | -328 | -347 | -218 | -354 | -30 | 0.0380060 | 0.1095890 | 0.1898239 | 0.0459821 | 6.1 | 6.1 | 6.0 | 4.0 | 6.0 | 3.9 | 8.5 | 0.001079 | 0.0010660 | 0.001002 | 0.000567 | 0.001048 | 0.000544 | 0.001642 | 6.5 | 6.5 | 6.4 | 3.9 | 6.4 | 3.9 | 9.3 | 0.000658 | 0.0006550 | 0.000645 | 0.000108 | 0.000638 | 0.000104 | 0.001223 | 354 | 391 | 2 | 9 |
| 71 | 16434 | -260 | -346 | -375 | -507 | -515 | -385 | -521 | -85 | 0.1183438 | 0.2906067 | 0.4363992 | 0.2487243 | 4.9 | 4.9 | 4.8 | 2.6 | 4.8 | 2.4 | 7.6 | 0.001166 | 0.0011590 | 0.001142 | 0.000600 | 0.001141 | 0.000592 | 0.001756 | 5.2 | 5.1 | 4.9 | 2.4 | 5.0 | 2.2 | 8.3 | 0.000660 | 0.0006560 | 0.000660 | 0.000114 | 0.000640 | 0.000107 | 0.001218 | 521 | 281 | 2 | 10 |
| 72 | 16436 | -319 | -174 | -135 | -12 | -544 | -144 | -481 | 0 | 0.2069214 | 0.4324853 | 0.5733855 | 0.4855193 | 9.2 | 9.1 | 8.9 | 7.2 | 9.0 | 7.1 | 11.4 | 0.000777 | 0.0007690 | 0.000744 | 0.000243 | 0.000752 | 0.000230 | 0.001341 | 9.9 | 9.8 | 9.7 | 7.5 | 9.7 | 7.4 | 12.5 | 0.000659 | 0.0006550 | 0.000649 | 0.000109 | 0.000638 | 0.000103 | 0.001222 | 638 | 245 | 2 | 3 |
| 73 | 16472 | -114 | -88 | -77 | -12 | -216 | -82 | -203 | 0 | 0.0689051 | 0.1952055 | 0.3228963 | 0.1289520 | 9.2 | 9.1 | 9.0 | 7.4 | 9.1 | 7.3 | 11.1 | 0.001099 | 0.0010920 | 0.001062 | 0.000513 | 0.001073 | 0.000497 | 0.001707 | 9.9 | 9.8 | 9.7 | 7.7 | 9.8 | 7.6 | 12.3 | 0.000660 | 0.0006570 | 0.000663 | 0.000110 | 0.000640 | 0.000096 | 0.001217 | 229 | 449 | 2 | 4 |
| 74 | 16476 | -300 | -375 | -402 | -564 | -591 | -414 | -600 | -79 | 0.0853847 | 0.2196673 | 0.3346380 | 0.1489175 | 5.3 | 5.2 | 5.0 | 3.3 | 5.2 | 3.2 | 7.6 | 0.000994 | 0.0009920 | 0.001025 | 0.000394 | 0.000972 | 0.000384 | 0.001601 | 5.6 | 5.5 | 5.4 | 3.3 | 5.5 | 3.1 | 8.3 | 0.000699 | 0.0006950 | 0.000692 | 0.000203 | 0.000680 | 0.000198 | 0.001210 | 600 | 1006 | 4 | 7 |
| 75 | 16508 | -239 | -261 | -270 | -452 | -468 | -281 | -476 | -35 | 0.0277063 | 0.0826810 | 0.1350294 | 0.0263665 | 6.2 | 6.1 | 6.0 | 4.4 | 6.1 | 4.3 | 8.2 | 0.000827 | 0.0008220 | 0.000810 | 0.000281 | 0.000805 | 0.000272 | 0.001395 | 6.6 | 6.5 | 6.5 | 4.6 | 6.5 | 4.5 | 8.8 | 0.000671 | 0.0006680 | 0.000675 | 0.000148 | 0.000651 | 0.000138 | 0.001212 | 478 | 748 | 3 | 5 |
| 76 | 16520 | -292 | -271 | -262 | -41 | -564 | -275 | -547 | -3 | 0.0194665 | 0.0606654 | 0.1037182 | 0.0188962 | 7.6 | 7.5 | 7.4 | 5.7 | 7.4 | 5.6 | 9.7 | 0.000512 | 0.0005050 | 0.000490 | 0.000145 | 0.000493 | 0.000133 | 0.000904 | 8.1 | 8.0 | 7.9 | 5.8 | 8.0 | 5.7 | 10.7 | 0.000660 | 0.0006570 | 0.000627 | 0.000108 | 0.000639 | 0.000100 | 0.001223 | 584 | 727 | 3 | 6 |
| 77 | 16523 | -246 | -277 | -289 | -427 | -482 | -300 | -491 | -41 | 0.0380060 | 0.1095890 | 0.1741683 | 0.0431251 | 6.4 | 6.3 | 6.2 | 4.2 | 6.2 | 4.0 | 8.8 | 0.000926 | 0.0009210 | 0.000927 | 0.000408 | 0.000904 | 0.000397 | 0.001462 | 6.8 | 6.7 | 6.5 | 4.2 | 6.6 | 4.0 | 9.7 | 0.000661 | 0.0006580 | 0.000654 | 0.000112 | 0.000641 | 0.000107 | 0.001226 | 492 | 638 | 3 | 7 |
| 78 | 16955 | -142 | -142 | -141 | -121 | -277 | -148 | -275 | -9 | 0.0153466 | 0.0264188 | 0.0371820 | 0.0075038 | 7.1 | 7.1 | 7.0 | 5.4 | 7.0 | 5.3 | 9.0 | 0.000704 | 0.0006980 | 0.000691 | 0.000245 | 0.000683 | 0.000232 | 0.001177 | 7.7 | 7.6 | 7.6 | 5.5 | 7.5 | 5.4 | 10.0 | 0.000660 | 0.0006570 | 0.000645 | 0.000108 | 0.000640 | 0.000100 | 0.001224 | 284 | 380 | 3 | 7 |
| 79 | 17514 | -292 | -262 | -250 | -162 | -561 | -263 | -540 | -2 | 0.0297662 | 0.0851272 | 0.1428571 | 0.0297216 | 7.7 | 7.6 | 7.5 | 5.8 | 7.6 | 5.7 | 10.0 | 0.000555 | 0.0005500 | 0.000530 | 0.000091 | 0.000535 | 0.000079 | 0.001038 | 8.3 | 8.2 | 7.9 | 5.9 | 8.1 | 5.8 | 11.0 | 0.000659 | 0.0006560 | 0.000656 | 0.000109 | 0.000639 | 0.000101 | 0.001222 | 583 | 0 | 1 | 5 |
| 80 | 20472 | -180 | -153 | -141 | -17 | -345 | -149 | -329 | 0 | 0.0421259 | 0.1242661 | 0.2172211 | 0.0578878 | 7.8 | 7.8 | 7.7 | 6.5 | 7.7 | 6.4 | 9.5 | 0.000744 | 0.0007380 | 0.000753 | 0.000241 | 0.000722 | 0.000230 | 0.001268 | 8.4 | 8.3 | 8.1 | 6.8 | 8.2 | 6.7 | 10.3 | 0.000710 | 0.0007060 | 0.000711 | 0.000203 | 0.000690 | 0.000194 | 0.001235 | 361 | 368 | 2 | 7 |
| 81 | 21214 | -252 | -274 | -282 | -328 | -491 | -293 | -501 | -40 | 0.0380060 | 0.1022505 | 0.1506849 | 0.0356211 | 8.3 | 8.2 | 8.2 | 6.3 | 8.2 | 6.2 | 10.5 | 0.001016 | 0.0010120 | 0.000992 | 0.000388 | 0.000992 | 0.000381 | 0.001655 | 9.1 | 9.1 | 8.9 | 6.9 | 9.0 | 6.9 | 11.6 | 0.000492 | 0.0004920 | 0.000518 | -0.000047 | 0.000475 | -0.000048 | 0.001028 | 504 | 313 | 2 | 5 |
| 82 | 21285 | -247 | -223 | -213 | -102 | -473 | -223 | -455 | -3 | 0.0359460 | 0.1022505 | 0.1506849 | 0.0357111 | 7.5 | 7.4 | 7.1 | 5.3 | 7.3 | 5.1 | 10.0 | 0.001061 | 0.0010520 | 0.001047 | 0.000493 | 0.001034 | 0.000476 | 0.001664 | 8.0 | 7.9 | 7.7 | 5.4 | 7.8 | 5.2 | 11.0 | 0.000659 | 0.0006550 | 0.000646 | 0.000111 | 0.000639 | 0.000108 | 0.001222 | 494 | 117 | 2 | 6 |
| 83 | 21308 | -90 | -97 | -100 | -166 | -177 | -105 | -179 | -11 | 0.0256463 | 0.0631115 | 0.1154599 | 0.0207268 | 6.3 | 6.3 | 6.2 | 5.0 | 6.2 | 4.9 | 7.7 | 0.001037 | 0.0010315 | 0.001004 | 0.000444 | 0.001013 | 0.000432 | 0.001648 | 6.8 | 6.8 | 6.7 | 5.3 | 6.8 | 5.3 | 8.4 | 0.000709 | 0.0007030 | 0.000687 | 0.000219 | 0.000688 | 0.000205 | 0.001214 | 181 | 232 | 2 | 7 |
| 84 | 21325 | -231 | -219 | -214 | -118 | -447 | -225 | -433 | -4 | 0.0215264 | 0.0557730 | 0.0802348 | 0.0139503 | 7.0 | 6.9 | 6.7 | 5.4 | 6.8 | 5.3 | 8.8 | 0.000762 | 0.0007550 | 0.000729 | 0.000160 | 0.000736 | 0.000149 | 0.001387 | 7.4 | 7.3 | 6.9 | 5.6 | 7.2 | 5.4 | 9.5 | 0.000767 | 0.0007610 | 0.000769 | 0.000258 | 0.000745 | 0.000251 | 0.001302 | 462 | 388 | 2 | 8 |
| 85 | 21433 | -112 | -106 | -102 | -14 | -217 | -108 | -211 | -3 | 0.0215264 | 0.0533268 | 0.0958904 | 0.0151934 | 8.1 | 8.1 | 8.1 | 6.5 | 8.0 | 6.5 | 9.8 | 0.000586 | 0.0005870 | 0.000585 | -0.000037 | 0.000568 | -0.000036 | 0.001206 | 8.7 | 8.7 | 8.5 | 6.7 | 8.6 | 6.7 | 10.9 | 0.000658 | 0.0006560 | 0.000647 | 0.000108 | 0.000639 | 0.000102 | 0.001215 | 224 | 373 | 2 | 5 |
| 86 | 22354 | -158 | -132 | -120 | -17 | -302 | -128 | -288 | 0 | 0.0441858 | 0.1316047 | 0.2407045 | 0.0679729 | 8.9 | 8.8 | 8.7 | 7.4 | 8.8 | 7.3 | 10.5 | 0.000533 | 0.0005280 | 0.000542 | 0.000046 | 0.000513 | 0.000039 | 0.001039 | 9.5 | 9.5 | 9.4 | 7.8 | 9.4 | 7.8 | 11.5 | 0.000672 | 0.0006690 | 0.000675 | 0.000141 | 0.000652 | 0.000132 | 0.001218 | 317 | 1856 | 3 | 4 |
| 87 | 23147 | -229 | -245 | -251 | -262 | -447 | -261 | -455 | -32 | 0.0277063 | 0.0704501 | 0.1037182 | 0.0200334 | 6.3 | 6.3 | 6.2 | 4.5 | 6.2 | 4.4 | 8.4 | 0.001029 | 0.0010210 | 0.001000 | 0.000472 | 0.001003 | 0.000453 | 0.001603 | 6.7 | 6.6 | 6.5 | 4.7 | 6.6 | 4.6 | 9.0 | 0.000682 | 0.0006780 | 0.000664 | 0.000154 | 0.000662 | 0.000146 | 0.001228 | 458 | 773 | 4 | 7 |
| 88 | 23212 | -311 | -442 | -475 | -607 | -616 | -484 | -622 | -157 | 0.2131012 | 0.4031311 | 0.5264188 | 0.4236566 | 5.9 | 5.9 | 5.7 | 3.7 | 5.8 | 3.7 | 8.2 | 0.001202 | 0.0011890 | 0.001151 | 0.000664 | 0.001170 | 0.000640 | 0.001787 | 6.4 | 6.3 | 6.3 | 4.1 | 6.2 | 4.0 | 8.9 | 0.000493 | 0.0004850 | 0.000458 | 0.000117 | 0.000472 | 0.000099 | 0.000902 | 622 | 1158 | 5 | 7 |
| 89 | 23727 | -336 | -379 | -398 | -631 | -659 | -412 | -671 | -57 | 0.0380060 | 0.1046967 | 0.1859100 | 0.0443488 | 6.0 | 5.9 | 5.9 | 4.1 | 5.9 | 4.0 | 8.2 | 0.000579 | 0.0005600 | 0.000518 | 0.000243 | 0.000548 | 0.000210 | 0.000983 | 6.4 | 6.3 | 6.1 | 4.2 | 6.2 | 4.0 | 8.8 | 0.000722 | 0.0007070 | 0.000676 | 0.000327 | 0.000693 | 0.000301 | 0.001166 | 672 | 1193 | 4 | 7 |
| 90 | 23777 | -232 | -196 | -181 | -19 | -442 | -192 | -422 | 0 | 0.0400659 | 0.1169276 | 0.2172211 | 0.0567561 | 7.7 | 7.6 | 7.4 | 6.3 | 7.5 | 6.2 | 9.3 | 0.000619 | 0.0006110 | 0.000590 | 0.000252 | 0.000599 | 0.000240 | 0.001021 | 8.1 | 8.0 | 7.8 | 6.5 | 8.0 | 6.4 | 10.0 | 0.000582 | 0.0005780 | 0.000567 | 0.000190 | 0.000565 | 0.000179 | 0.000990 | 463 | 280 | 2 | 8 |
| 91 | 23795 | -229 | -192 | -178 | -41 | -434 | -188 | -412 | 0 | 0.0524256 | 0.1413894 | 0.2211350 | 0.0675207 | 7.5 | 7.4 | 7.2 | 5.8 | 7.3 | 5.6 | 9.6 | 0.001000 | 0.0009900 | 0.000983 | 0.000615 | 0.000978 | 0.000597 | 0.001418 | 8.0 | 7.8 | 7.5 | 6.0 | 7.8 | 5.8 | 10.4 | 0.000875 | 0.0008680 | 0.000859 | 0.000481 | 0.000855 | 0.000470 | 0.001294 | 458 | 360 | 2 | 10 |
| 92 | 23797 | -231 | -188 | -171 | -14 | -436 | -181 | -412 | 0 | 0.0606654 | 0.1609589 | 0.2602740 | 0.0868883 | 7.8 | 7.7 | 7.5 | 6.2 | 7.7 | 6.0 | 9.8 | 0.000903 | 0.0008930 | 0.000877 | 0.000535 | 0.000880 | 0.000516 | 0.001310 | 8.4 | 8.3 | 8.0 | 6.5 | 8.2 | 6.4 | 10.6 | 0.000689 | 0.0006830 | 0.000660 | 0.000316 | 0.000671 | 0.000307 | 0.001080 | 462 | 1007 | 2 | 6 |
| 93 | 23806 | -238 | -182 | -161 | -12 | -446 | -171 | -419 | 0 | 0.0750850 | 0.2098826 | 0.3228963 | 0.1334865 | 8.2 | 8.1 | 7.9 | 6.3 | 8.1 | 6.1 | 10.5 | 0.000910 | 0.0009020 | 0.000883 | 0.000329 | 0.000883 | 0.000313 | 0.001524 | 8.9 | 8.8 | 8.7 | 6.5 | 8.7 | 6.3 | 11.6 | 0.000659 | 0.0006570 | 0.000658 | 0.000109 | 0.000639 | 0.000102 | 0.001220 | 477 | 1090 | 4 | 7 |
| 94 | 23813 | -139 | -90 | -73 | -11 | -253 | -78 | -232 | 0 | 0.1204037 | 0.3028376 | 0.4637965 | 0.2816816 | 10.1 | 10.0 | 9.9 | 8.3 | 10.0 | 8.2 | 12.0 | 0.000884 | 0.0008780 | 0.000879 | 0.000354 | 0.000861 | 0.000344 | 0.001436 | 10.9 | 10.9 | 10.7 | 8.7 | 10.8 | 8.6 | 13.3 | 0.000659 | 0.0006560 | 0.000668 | 0.000105 | 0.000639 | 0.000099 | 0.001220 | 278 | 1004 | 4 | 7 |
| 95 | 23816 | -240 | -238 | -238 | -206 | -466 | -249 | -463 | -13 | 0.0112267 | 0.0264188 | 0.0450098 | 0.0076469 | 7.1 | 7.1 | 7.0 | 5.1 | 7.0 | 5.0 | 9.4 | 0.000647 | 0.0006350 | 0.000610 | 0.000214 | 0.000620 | 0.000192 | 0.001126 | 7.6 | 7.6 | 7.5 | 5.2 | 7.5 | 5.1 | 10.3 | 0.000659 | 0.0006550 | 0.000630 | 0.000112 | 0.000638 | 0.000105 | 0.001215 | 479 | 891 | 2 | 8 |
| 96 | 23823 | -184 | -167 | -160 | -16 | -356 | -168 | -344 | -2 | 0.0235864 | 0.0777886 | 0.1389432 | 0.0255540 | 8.0 | 8.0 | 7.9 | 6.5 | 7.9 | 6.4 | 9.7 | 0.000514 | 0.0005100 | 0.000508 | 0.000217 | 0.000500 | 0.000208 | 0.000828 | 8.6 | 8.5 | 8.5 | 6.8 | 8.5 | 6.7 | 10.5 | 0.000672 | 0.0006690 | 0.000630 | 0.000145 | 0.000652 | 0.000137 | 0.001211 | 369 | 1096 | 2 | 2 |
| 97 | 23839 | -240 | -222 | -214 | -141 | -462 | -225 | -449 | -3 | 0.0215264 | 0.0655577 | 0.1115460 | 0.0195415 | 7.7 | 7.6 | 7.3 | 5.8 | 7.5 | 5.6 | 9.8 | 0.000650 | 0.0006440 | 0.000630 | 0.000265 | 0.000631 | 0.000254 | 0.001057 | 8.2 | 8.1 | 8.0 | 5.9 | 8.1 | 5.8 | 10.8 | 0.000659 | 0.0006550 | 0.000626 | 0.000110 | 0.000638 | 0.000102 | 0.001220 | 479 | 1008 | 3 | 9 |
| 98 | 23845 | -56 | -53 | -51 | -12 | -108 | -54 | -105 | -2 | 0.0277063 | 0.0557730 | 0.0998043 | 0.0209459 | 8.6 | 8.6 | 8.6 | 7.2 | 8.6 | 7.2 | 10.1 | 0.001050 | 0.0010370 | 0.001019 | 0.000617 | 0.001023 | 0.000593 | 0.001527 | 9.4 | 9.4 | 9.4 | 7.8 | 9.3 | 7.8 | 11.0 | 0.000650 | 0.0006460 | 0.000637 | 0.000106 | 0.000630 | 0.000100 | 0.001203 | 112 | 1057 | 3 | 8 |
| 99 | 23850 | -190 | -234 | -253 | -369 | -376 | -261 | -381 | -40 | 0.0565455 | 0.1805284 | 0.3228963 | 0.1216061 | 3.9 | 3.8 | 3.7 | 1.9 | 3.8 | 1.8 | 6.1 | 0.000897 | 0.0008850 | 0.000857 | 0.000277 | 0.000865 | 0.000259 | 0.001557 | 4.1 | 4.0 | 4.0 | 1.6 | 3.9 | 1.5 | 6.7 | 0.000660 | 0.0006570 | 0.000630 | 0.000110 | 0.000639 | 0.000105 | 0.001220 | 381 | 89 | 2 | 8 |
| 100 | 23983 | -238 | -149 | -124 | -15 | -420 | -132 | -379 | 0 | 0.1616026 | 0.3493151 | 0.4755382 | 0.3273804 | 8.8 | 8.7 | 8.6 | 6.9 | 8.6 | 6.7 | 11.0 | 0.000992 | 0.0009800 | 0.000971 | 0.000544 | 0.000964 | 0.000524 | 0.001483 | 9.5 | 9.4 | 9.3 | 7.1 | 9.3 | 7.0 | 12.2 | 0.000660 | 0.0006580 | 0.000615 | 0.000108 | 0.000640 | 0.000104 | 0.001223 | 476 | 698 | 2 | 3 |
| 101 | 23988 | -203 | -178 | -168 | -14 | -389 | -177 | -373 | -1 | 0.0359460 | 0.1046967 | 0.1780822 | 0.0409534 | 7.9 | 7.9 | 7.6 | 6.0 | 7.8 | 5.9 | 10.1 | 0.000811 | 0.0008000 | 0.000781 | 0.000376 | 0.000785 | 0.000354 | 0.001284 | 8.5 | 8.5 | 8.3 | 6.2 | 8.4 | 6.1 | 11.2 | 0.000660 | 0.0006560 | 0.000657 | 0.000113 | 0.000640 | 0.000107 | 0.001218 | 406 | 727 | 3 | 7 |
| 102 | 24003 | -286 | -149 | -119 | -15 | -458 | -126 | -398 | 0 | 0.2687197 | 0.4716243 | 0.5812133 | 0.5567829 | 10.1 | 10.0 | 9.8 | 8.6 | 10.0 | 8.5 | 12.0 | 0.000856 | 0.0008490 | 0.000852 | 0.000252 | 0.000829 | 0.000245 | 0.001490 | 11.2 | 11.1 | 10.9 | 9.5 | 11.0 | 9.4 | 13.2 | 0.000512 | 0.0005090 | 0.000485 | -0.000007 | 0.000493 | -0.000009 | 0.001050 | 573 | 730 | 4 | 7 |
| 103 | 24024 | -208 | -266 | -288 | -401 | -410 | -296 | -415 | -54 | 0.0833248 | 0.2343444 | 0.3855186 | 0.1806715 | 4.6 | 4.5 | 4.4 | 2.4 | 4.5 | 2.3 | 7.1 | 0.001102 | 0.0010950 | 0.001127 | 0.000493 | 0.001075 | 0.000478 | 0.001741 | 4.8 | 4.8 | 4.8 | 2.1 | 4.7 | 2.0 | 7.7 | 0.000660 | 0.0006560 | 0.000657 | 0.000108 | 0.000639 | 0.000098 | 0.001217 | 415 | 700 | 3 | 7 |
| 104 | 24038 | -140 | -134 | -132 | -42 | -273 | -139 | -266 | -4 | 0.0174065 | 0.0410959 | 0.0724070 | 0.0112020 | 7.7 | 7.7 | 7.6 | 6.0 | 7.6 | 5.9 | 9.6 | 0.000642 | 0.0006380 | 0.000620 | 0.000201 | 0.000624 | 0.000193 | 0.001101 | 8.3 | 8.3 | 8.1 | 6.2 | 8.2 | 6.1 | 10.6 | 0.000659 | 0.0006550 | 0.000638 | 0.000108 | 0.000639 | 0.000106 | 0.001226 | 281 | 360 | 2 | 6 |
| 105 | 24045 | -140 | -134 | -131 | -79 | -272 | -138 | -265 | -4 | 0.0194665 | 0.0435421 | 0.0724070 | 0.0112240 | 7.6 | 7.5 | 7.4 | 5.8 | 7.4 | 5.7 | 9.5 | 0.000809 | 0.0008060 | 0.000796 | 0.000360 | 0.000792 | 0.000349 | 0.001265 | 8.1 | 8.1 | 8.0 | 5.9 | 8.0 | 5.8 | 10.5 | 0.000661 | 0.0006560 | 0.000640 | 0.000111 | 0.000640 | 0.000110 | 0.001231 | 280 | 682 | 3 | 7 |
| 106 | 24049 | -148 | -137 | -133 | -41 | -285 | -140 | -277 | -3 | 0.0235864 | 0.0631115 | 0.1115460 | 0.0178765 | 7.8 | 7.7 | 7.6 | 5.9 | 7.7 | 5.9 | 9.8 | 0.000912 | 0.0009070 | 0.000872 | 0.000447 | 0.000891 | 0.000440 | 0.001398 | 8.4 | 8.3 | 8.3 | 6.1 | 8.2 | 6.0 | 10.8 | 0.000661 | 0.0006580 | 0.000633 | 0.000105 | 0.000640 | 0.000098 | 0.001222 | 295 | 700 | 3 | 7 |
| 107 | 24051 | -132 | -129 | -129 | -84 | -256 | -135 | -253 | -7 | 0.0153466 | 0.0264188 | 0.0332681 | 0.0074700 | 7.3 | 7.3 | 7.1 | 5.7 | 7.2 | 5.6 | 9.1 | 0.000533 | 0.0005330 | 0.000540 | -0.000052 | 0.000515 | -0.000054 | 0.001116 | 7.8 | 7.8 | 7.7 | 5.8 | 7.7 | 5.7 | 10.1 | 0.000659 | 0.0006550 | 0.000661 | 0.000107 | 0.000638 | 0.000101 | 0.001226 | 263 | 1314 | 4 | 2 |
| 108 | 24068 | -260 | -230 | -218 | -158 | -497 | -229 | -476 | -1 | 0.0359460 | 0.1071429 | 0.1624266 | 0.0399069 | 7.6 | 7.5 | 7.3 | 5.6 | 7.5 | 5.4 | 10.1 | 0.000876 | 0.0008680 | 0.000847 | 0.000389 | 0.000852 | 0.000372 | 0.001391 | 8.2 | 8.1 | 7.8 | 5.7 | 8.0 | 5.5 | 11.0 | 0.000659 | 0.0006560 | 0.000635 | 0.000111 | 0.000639 | 0.000104 | 0.001224 | 519 | 1074 | 2 | 9 |
| 109 | 24072 | -270 | -220 | -201 | -57 | -510 | -212 | -482 | 0 | 0.0627253 | 0.1731898 | 0.2563601 | 0.0910061 | 7.9 | 7.8 | 7.7 | 5.8 | 7.7 | 5.6 | 10.3 | 0.000932 | 0.0009240 | 0.000886 | 0.000455 | 0.000908 | 0.000441 | 0.001440 | 8.5 | 8.4 | 8.0 | 5.9 | 8.3 | 5.8 | 11.3 | 0.000660 | 0.0006580 | 0.000665 | 0.000113 | 0.000641 | 0.000106 | 0.001218 | 541 | 797 | 2 | 8 |
| 110 | 24085 | -27 | -28 | -28 | -41 | -53 | -29 | -52 | -3 | 0.0297662 | 0.0655577 | 0.0802348 | 0.0275948 | 4.1 | 4.1 | 4.0 | 2.8 | 4.0 | 2.7 | 5.5 | 0.000420 | 0.0004140 | 0.000413 | -0.000034 | 0.000399 | -0.000047 | 0.000893 | 4.3 | 4.3 | 4.2 | 2.4 | 4.2 | 2.4 | 6.1 | 0.000660 | 0.0006560 | 0.000648 | 0.000109 | 0.000639 | 0.000102 | 0.001217 | 54 | 387 | 2 | 7 |
| 111 | 24105 | -76 | -68 | -64 | -13 | -146 | -67 | -141 | -1 | 0.0318261 | 0.0875734 | 0.1624266 | 0.0364528 | 8.8 | 8.8 | 8.8 | 7.0 | 8.7 | 7.0 | 10.7 | 0.000953 | 0.0009400 | 0.000922 | 0.000542 | 0.000926 | 0.000517 | 0.001410 | 9.5 | 9.5 | 9.4 | 7.3 | 9.4 | 7.3 | 11.8 | 0.000657 | 0.0006540 | 0.000648 | 0.000111 | 0.000638 | 0.000108 | 0.001213 | 152 | 273 | 2 | 9 |
| 112 | 24121 | -117 | -95 | -86 | -15 | -223 | -91 | -211 | 0 | 0.0503656 | 0.1487280 | 0.2602740 | 0.0827908 | 8.1 | 8.0 | 8.0 | 6.5 | 8.0 | 6.5 | 9.7 | 0.001152 | 0.0011390 | 0.001132 | 0.000686 | 0.001123 | 0.000659 | 0.001660 | 8.6 | 8.6 | 8.5 | 6.8 | 8.5 | 6.8 | 10.5 | 0.000688 | 0.0006830 | 0.000641 | 0.000146 | 0.000666 | 0.000140 | 0.001248 | 234 | 392 | 2 | 9 |
| 113 | 24124 | -353 | -270 | -241 | -95 | -657 | -254 | -615 | 0 | 0.0833248 | 0.2221135 | 0.3228963 | 0.1408676 | 7.8 | 7.7 | 7.5 | 5.6 | 7.6 | 5.5 | 10.4 | 0.000905 | 0.0008930 | 0.000872 | 0.000351 | 0.000875 | 0.000331 | 0.001497 | 8.4 | 8.3 | 8.0 | 5.8 | 8.2 | 5.6 | 11.4 | 0.000661 | 0.0006580 | 0.000675 | 0.000110 | 0.000641 | 0.000105 | 0.001221 | 706 | 0 | 1 | 7 |
| 114 | 24127 | -228 | -154 | -127 | -13 | -420 | -135 | -387 | 0 | 0.1059841 | 0.2832681 | 0.4403131 | 0.2434882 | 9.2 | 9.1 | 9.0 | 7.4 | 9.1 | 7.2 | 11.3 | 0.000733 | 0.0007270 | 0.000723 | 0.000151 | 0.000708 | 0.000135 | 0.001330 | 9.9 | 9.9 | 9.7 | 7.7 | 9.8 | 7.5 | 12.5 | 0.000659 | 0.0006570 | 0.000661 | 0.000109 | 0.000640 | 0.000109 | 0.001225 | 456 | 1072 | 4 | 7 |
| 115 | 24129 | -174 | -127 | -110 | -22 | -322 | -117 | -299 | 0 | 0.0977444 | 0.2441292 | 0.3659491 | 0.1776414 | 8.5 | 8.4 | 8.4 | 6.5 | 8.4 | 6.4 | 10.8 | 0.001216 | 0.0012070 | 0.001198 | 0.000650 | 0.001188 | 0.000631 | 0.001815 | 9.2 | 9.1 | 9.0 | 6.8 | 9.0 | 6.6 | 11.9 | 0.000659 | 0.0006560 | 0.000620 | 0.000107 | 0.000638 | 0.000095 | 0.001218 | 349 | 727 | 3 | 7 |
| 116 | 24148 | -198 | -182 | -175 | -56 | -381 | -185 | -369 | -3 | 0.0235864 | 0.0728963 | 0.1154599 | 0.0216339 | 7.7 | 7.7 | 7.6 | 5.7 | 7.6 | 5.6 | 10.0 | 0.000822 | 0.0008200 | 0.000806 | 0.000153 | 0.000800 | 0.000145 | 0.001502 | 8.3 | 8.2 | 8.3 | 5.8 | 8.2 | 5.7 | 11.0 | 0.000659 | 0.0006560 | 0.000663 | 0.000110 | 0.000639 | 0.000100 | 0.001219 | 395 | 330 | 2 | 7 |
| 117 | 24156 | -259 | -227 | -215 | -161 | -493 | -226 | -468 | -1 | 0.0503656 | 0.1291585 | 0.1859100 | 0.0540020 | 7.5 | 7.4 | 7.1 | 5.3 | 7.4 | 5.1 | 10.1 | 0.001168 | 0.0011580 | 0.001130 | 0.000702 | 0.001142 | 0.000684 | 0.001680 | 8.1 | 8.0 | 7.6 | 5.4 | 7.9 | 5.2 | 11.1 | 0.000659 | 0.0006560 | 0.000659 | 0.000112 | 0.000639 | 0.000103 | 0.001216 | 518 | 1075 | 4 | 8 |
| 118 | 24162 | -200 | -183 | -175 | -14 | -388 | -185 | -374 | -2 | 0.0215264 | 0.0655577 | 0.1232877 | 0.0221093 | 8.2 | 8.2 | 8.1 | 6.5 | 8.1 | 6.4 | 10.1 | 0.000398 | 0.0003950 | 0.000387 | 0.000007 | 0.000383 | -0.000002 | 0.000797 | 8.9 | 8.8 | 8.7 | 6.7 | 8.8 | 6.6 | 11.2 | 0.000661 | 0.0006580 | 0.000674 | 0.000113 | 0.000641 | 0.000109 | 0.001228 | 401 | 321 | 2 | 6 |
| 119 | 24269 | -204 | -198 | -195 | -172 | -395 | -205 | -388 | -8 | 0.0194665 | 0.0508806 | 0.0841487 | 0.0131010 | 7.3 | 7.2 | 7.1 | 5.2 | 7.1 | 5.1 | 9.7 | 0.001081 | 0.0010750 | 0.001058 | 0.000493 | 0.001056 | 0.000480 | 0.001693 | 7.8 | 7.7 | 7.5 | 5.3 | 7.6 | 5.2 | 10.7 | 0.000660 | 0.0006570 | 0.000631 | 0.000106 | 0.000640 | 0.000101 | 0.001219 | 408 | 1106 | 6 | 4 |
| 120 | 24375 | -203 | -183 | -173 | -14 | -391 | -183 | -377 | -2 | 0.0256463 | 0.0802348 | 0.1506849 | 0.0282948 | 8.0 | 8.0 | 7.8 | 6.2 | 7.9 | 6.1 | 10.0 | 0.000583 | 0.0005780 | 0.000551 | 0.000227 | 0.000566 | 0.000216 | 0.000958 | 8.6 | 8.6 | 8.4 | 6.4 | 8.5 | 6.3 | 11.1 | 0.000659 | 0.0006560 | 0.000650 | 0.000108 | 0.000639 | 0.000105 | 0.001228 | 406 | 388 | 2 | 6 |
| 121 | 24409 | -156 | -159 | -160 | -151 | -305 | -168 | -307 | -15 | 0.0174065 | 0.0288650 | 0.0450098 | 0.0083605 | 7.0 | 6.9 | 6.8 | 5.1 | 6.8 | 5.0 | 9.0 | 0.000835 | 0.0008320 | 0.000819 | 0.000233 | 0.000813 | 0.000225 | 0.001442 | 7.4 | 7.4 | 7.4 | 5.2 | 7.3 | 5.1 | 9.9 | 0.000658 | 0.0006550 | 0.000623 | 0.000111 | 0.000638 | 0.000109 | 0.001219 | 313 | 357 | 2 | 9 |
| 122 | 24440 | -336 | -360 | -367 | -427 | -655 | -382 | -669 | -53 | 0.0359460 | 0.0875734 | 0.1232877 | 0.0294070 | 6.7 | 6.6 | 6.5 | 4.9 | 6.6 | 4.8 | 8.9 | 0.000824 | 0.0008160 | 0.000807 | 0.000447 | 0.000803 | 0.000430 | 0.001234 | 7.3 | 7.2 | 6.9 | 5.3 | 7.1 | 5.2 | 9.7 | 0.000572 | 0.0005720 | 0.000574 | 0.000148 | 0.000559 | 0.000144 | 0.000996 | 673 | 738 | 3 | 8 |
| 123 | 24912 | -352 | -313 | -297 | -220 | -673 | -312 | -647 | -3 | 0.0359460 | 0.1046967 | 0.1663405 | 0.0405083 | 7.5 | 7.3 | 7.1 | 5.3 | 7.3 | 5.1 | 10.0 | 0.000735 | 0.0007260 | 0.000709 | 0.000192 | 0.000709 | 0.000178 | 0.001309 | 8.0 | 7.9 | 7.9 | 5.4 | 7.8 | 5.2 | 11.0 | 0.000660 | 0.0006560 | 0.000622 | 0.000109 | 0.000639 | 0.000103 | 0.001220 | 703 | 393 | 2 | 6 |
| 124 | 24925 | -196 | -134 | -114 | -15 | -358 | -121 | -328 | 0 | 0.1162839 | 0.2808219 | 0.4129159 | 0.2277449 | 8.7 | 8.6 | 8.5 | 6.7 | 8.5 | 6.6 | 10.9 | 0.001095 | 0.0010860 | 0.001060 | 0.000524 | 0.001067 | 0.000509 | 0.001693 | 9.3 | 9.3 | 9.2 | 6.9 | 9.2 | 6.8 | 12.0 | 0.000660 | 0.0006580 | 0.000665 | 0.000105 | 0.000640 | 0.000101 | 0.001223 | 391 | 484 | 2 | 3 |
| 125 | 2543F | -248 | -189 | -165 | -14 | -468 | -175 | -440 | 0 | 0.0689051 | 0.2000978 | 0.3346380 | 0.1338122 | 8.7 | 8.7 | 8.6 | 6.9 | 8.6 | 6.8 | 10.9 | 0.000629 | 0.0006230 | 0.000603 | 0.000226 | 0.000609 | 0.000212 | 0.001055 | 9.4 | 9.4 | 9.1 | 7.1 | 9.3 | 7.0 | 12.0 | 0.000659 | 0.0006560 | 0.000652 | 0.000109 | 0.000639 | 0.000106 | 0.001221 | 497 | 1375 | 2 | 4 |
| 126 | 25E35 | -175 | -181 | -184 | -184 | -341 | -192 | -345 | -19 | 0.0194665 | 0.0459883 | 0.0606654 | 0.0108254 | 6.9 | 6.8 | 6.7 | 4.9 | 6.7 | 4.8 | 9.1 | 0.000921 | 0.0009170 | 0.000898 | 0.000345 | 0.000899 | 0.000334 | 0.001512 | 7.3 | 7.3 | 7.2 | 4.9 | 7.2 | 4.8 | 10.0 | 0.000659 | 0.0006550 | 0.000648 | 0.000107 | 0.000638 | 0.000104 | 0.001221 | 350 | 823 | 2 | 6 |
| 127 | 26070 | -231 | -285 | -305 | -442 | -455 | -314 | -462 | -56 | 0.0750850 | 0.1976517 | 0.3189824 | 0.1270062 | 5.7 | 5.6 | 5.4 | 3.8 | 5.6 | 3.7 | 7.7 | 0.001024 | 0.0010180 | 0.001009 | 0.000455 | 0.001000 | 0.000442 | 0.001613 | 6.1 | 6.0 | 6.0 | 4.0 | 6.0 | 3.9 | 8.3 | 0.000637 | 0.0006340 | 0.000608 | 0.000104 | 0.000618 | 0.000101 | 0.001177 | 462 | 1848 | 6 | 6 |
| 128 | 26079 | -199 | -232 | -246 | -386 | -392 | -255 | -398 | -35 | 0.0400659 | 0.1242661 | 0.2289628 | 0.0651468 | 5.1 | 5.1 | 4.9 | 3.4 | 5.0 | 3.3 | 7.0 | 0.000860 | 0.0008430 | 0.000768 | 0.000361 | 0.000826 | 0.000337 | 0.001424 | 5.2 | 5.2 | 5.2 | 3.2 | 5.1 | 3.1 | 7.4 | 0.000877 | 0.0008630 | 0.000834 | 0.000427 | 0.000848 | 0.000407 | 0.001374 | 398 | 1393 | 3 | 7 |
| 129 | 26082 | -126 | -122 | -120 | -39 | -245 | -127 | -241 | -5 | 0.0174065 | 0.0362035 | 0.0567515 | 0.0091067 | 7.4 | 7.3 | 7.3 | 5.9 | 7.3 | 5.9 | 8.9 | 0.000682 | 0.0006720 | 0.000669 | 0.000254 | 0.000657 | 0.000236 | 0.001145 | 7.9 | 7.9 | 7.8 | 6.3 | 7.9 | 6.2 | 9.7 | 0.000650 | 0.0006450 | 0.000631 | 0.000262 | 0.000632 | 0.000250 | 0.001061 | 252 | 162 | 2 | 1 |
| 130 | 26190 | -174 | -173 | -173 | -143 | -339 | -181 | -335 | -9 | 0.0153466 | 0.0288650 | 0.0410959 | 0.0075217 | 7.2 | 7.2 | 7.2 | 5.6 | 7.1 | 5.5 | 9.1 | 0.000749 | 0.0007400 | 0.000725 | 0.000445 | 0.000730 | 0.000426 | 0.001085 | 7.9 | 7.8 | 7.7 | 5.9 | 7.7 | 5.8 | 10.0 | 0.000547 | 0.0005460 | 0.000553 | 0.000081 | 0.000531 | 0.000080 | 0.001016 | 349 | 1834 | 6 | 8 |
| 131 | 26195 | -150 | -135 | -128 | -26 | -289 | -135 | -277 | -1 | 0.0318261 | 0.0875734 | 0.1545988 | 0.0311070 | 7.7 | 7.7 | 7.6 | 6.1 | 7.6 | 6.1 | 9.5 | 0.001012 | 0.0009980 | 0.000966 | 0.000598 | 0.000984 | 0.000572 | 0.001473 | 8.3 | 8.2 | 8.1 | 6.5 | 8.2 | 6.4 | 10.3 | 0.000670 | 0.0006670 | 0.000640 | 0.000133 | 0.000650 | 0.000130 | 0.001224 | 300 | 1486 | 5 | 8 |
| 132 | 26196 | -229 | -303 | -331 | -445 | -453 | -340 | -458 | -66 | 0.0998043 | 0.2734834 | 0.4403131 | 0.2389961 | 3.5 | 3.4 | 3.4 | 1.9 | 3.4 | 1.9 | 5.2 | 0.001057 | 0.0010540 | 0.001061 | 0.000399 | 0.001033 | 0.000391 | 0.001725 | 3.6 | 3.6 | 3.5 | 2.0 | 3.5 | 1.9 | 5.4 | 0.000644 | 0.0006420 | 0.000637 | 0.000109 | 0.000625 | 0.000101 | 0.001184 | 458 | 1858 | 3 | 3 |
| 133 | 26225 | -130 | -117 | -110 | -13 | -252 | -116 | -243 | -1 | 0.0256463 | 0.0851272 | 0.1585127 | 0.0320514 | 8.1 | 8.1 | 8.0 | 6.5 | 8.0 | 6.5 | 9.9 | 0.000765 | 0.0007580 | 0.000719 | 0.000481 | 0.000748 | 0.000467 | 0.001073 | 8.7 | 8.7 | 8.7 | 6.9 | 8.6 | 6.8 | 10.8 | 0.000662 | 0.0006580 | 0.000639 | 0.000198 | 0.000644 | 0.000195 | 0.001141 | 261 | 362 | 2 | 5 |
| 134 | 26231 | -175 | -144 | -131 | -15 | -333 | -139 | -316 | 0 | 0.0503656 | 0.1438356 | 0.2485323 | 0.0763740 | 8.9 | 8.9 | 8.8 | 7.4 | 8.8 | 7.3 | 10.5 | 0.000548 | 0.0005450 | 0.000528 | 0.000324 | 0.000537 | 0.000319 | 0.000788 | 9.6 | 9.5 | 9.5 | 7.8 | 9.5 | 7.8 | 11.5 | 0.000672 | 0.0006680 | 0.000669 | 0.000143 | 0.000651 | 0.000134 | 0.001220 | 350 | 364 | 2 | 5 |
| 135 | 31439 | -238 | -280 | -299 | -464 | -468 | -309 | -476 | -46 | 0.0483057 | 0.1413894 | 0.2524462 | 0.0760627 | 5.1 | 5.1 | 4.9 | 3.3 | 5.0 | 3.2 | 7.2 | 0.000866 | 0.0008520 | 0.000826 | 0.000381 | 0.000836 | 0.000360 | 0.001406 | 5.4 | 5.3 | 5.1 | 3.2 | 5.2 | 3.0 | 7.8 | 0.000768 | 0.0007620 | 0.000755 | 0.000276 | 0.000746 | 0.000262 | 0.001286 | 476 | 1486 | 4 | 4 |
| 136 | 32E5F | -116 | -127 | -132 | -217 | -226 | -137 | -230 | -17 | 0.0297662 | 0.0802348 | 0.1467710 | 0.0289864 | 6.7 | 6.7 | 6.6 | 5.2 | 6.6 | 5.1 | 8.3 | 0.001049 | 0.0010430 | 0.001041 | 0.000475 | 0.001025 | 0.000458 | 0.001640 | 7.3 | 7.2 | 7.1 | 5.5 | 7.2 | 5.5 | 9.1 | 0.000626 | 0.0006250 | 0.000639 | 0.000081 | 0.000608 | 0.000076 | 0.001173 | 231 | 1777 | 5 | 3 |
| 137 | 3336E | -114 | -123 | -127 | -214 | -224 | -132 | -226 | -14 | 0.0256463 | 0.0631115 | 0.1076321 | 0.0196681 | 6.3 | 6.3 | 6.2 | 4.8 | 6.2 | 4.8 | 7.9 | 0.000991 | 0.0009860 | 0.001021 | 0.000413 | 0.000967 | 0.000403 | 0.001594 | 6.8 | 6.7 | 6.6 | 5.1 | 6.7 | 5.0 | 8.6 | 0.000652 | 0.0006490 | 0.000652 | 0.000107 | 0.000632 | 0.000101 | 0.001203 | 229 | 1889 | 5 | 3 |
| 138 | 3360A | -176 | -199 | -209 | -326 | -346 | -217 | -352 | -29 | 0.0359460 | 0.1071429 | 0.1859100 | 0.0436031 | 5.9 | 5.9 | 5.8 | 4.3 | 5.8 | 4.3 | 7.7 | 0.001060 | 0.0010470 | 0.001000 | 0.000640 | 0.001033 | 0.000617 | 0.001524 | 6.3 | 6.3 | 6.2 | 4.5 | 6.2 | 4.5 | 8.3 | 0.000592 | 0.0005850 | 0.000573 | 0.000263 | 0.000575 | 0.000249 | 0.000940 | 353 | 391 | 3 | 4 |
| 139 | 33816 | -288 | -359 | -385 | -544 | -569 | -396 | -577 | -72 | 0.0792049 | 0.2147750 | 0.3307241 | 0.1412544 | 5.3 | 5.2 | 5.0 | 3.5 | 5.1 | 3.3 | 7.4 | 0.000986 | 0.0009750 | 0.000964 | 0.000522 | 0.000960 | 0.000501 | 0.001490 | 5.6 | 5.5 | 5.3 | 3.6 | 5.4 | 3.5 | 7.9 | 0.000690 | 0.0006840 | 0.000673 | 0.000280 | 0.000671 | 0.000271 | 0.001122 | 577 | 730 | 2 | 3 |
| 140 | 33B58 | -230 | -311 | -339 | -448 | -455 | -347 | -460 | -78 | 0.1265836 | 0.3126223 | 0.4677104 | 0.2864826 | 4.4 | 4.3 | 4.3 | 2.6 | 4.3 | 2.5 | 6.4 | 0.001259 | 0.0012460 | 0.001226 | 0.000728 | 0.001229 | 0.000708 | 0.001839 | 4.6 | 4.6 | 4.3 | 2.7 | 4.5 | 2.6 | 6.8 | 0.000882 | 0.0008710 | 0.000860 | 0.000452 | 0.000857 | 0.000435 | 0.001346 | 460 | 1504 | 3 | 7 |
| 141 | 35252 | -147 | -174 | -185 | -281 | -289 | -191 | -294 | -29 | 0.0503656 | 0.1462818 | 0.2563601 | 0.0798228 | 5.7 | 5.7 | 5.5 | 4.2 | 5.6 | 4.2 | 7.4 | 0.001150 | 0.0011440 | 0.001130 | 0.000599 | 0.001126 | 0.000590 | 0.001725 | 6.2 | 6.1 | 6.1 | 4.6 | 6.1 | 4.5 | 8.0 | 0.000689 | 0.0006850 | 0.000674 | 0.000228 | 0.000670 | 0.000222 | 0.001168 | 294 | 1054 | 2 | 3 |
| 142 | 3581A | -256 | -326 | -354 | -498 | -505 | -364 | -512 | -64 | 0.0792049 | 0.2343444 | 0.3737769 | 0.1742568 | 5.1 | 5.0 | 4.8 | 2.8 | 4.9 | 2.6 | 7.6 | 0.000992 | 0.0009850 | 0.000964 | 0.000399 | 0.000966 | 0.000385 | 0.001607 | 5.3 | 5.3 | 5.0 | 2.6 | 5.2 | 2.5 | 8.4 | 0.000660 | 0.0006560 | 0.000626 | 0.000112 | 0.000639 | 0.000106 | 0.001219 | 512 | 738 | 2 | 6 |
| 143 | 3604E | -238 | -195 | -176 | -14 | -453 | -187 | -430 | 0 | 0.0503656 | 0.1487280 | 0.2602740 | 0.0817012 | 8.5 | 8.4 | 8.4 | 6.9 | 8.4 | 6.8 | 10.2 | 0.000490 | 0.0004830 | 0.000471 | 0.000257 | 0.000475 | 0.000245 | 0.000749 | 9.2 | 9.1 | 9.0 | 7.4 | 9.1 | 7.3 | 11.2 | 0.000493 | 0.0004840 | 0.000468 | 0.000183 | 0.000474 | 0.000166 | 0.000832 | 477 | 1345 | 5 | 4 |
| 144 | 36401 | -182 | -217 | -232 | -351 | -359 | -240 | -365 | -36 | 0.0503656 | 0.1462818 | 0.2641879 | 0.0828068 | 5.0 | 5.0 | 4.9 | 3.3 | 4.9 | 3.3 | 6.8 | 0.000976 | 0.0009640 | 0.000903 | 0.000442 | 0.000946 | 0.000422 | 0.001558 | 5.3 | 5.2 | 5.2 | 3.4 | 5.2 | 3.3 | 7.3 | 0.000672 | 0.0006690 | 0.000621 | 0.000137 | 0.000652 | 0.000130 | 0.001219 | 365 | 57 | 2 | 5 |
| 145 | 36417 | -306 | -271 | -256 | -42 | -587 | -270 | -564 | -1 | 0.0297662 | 0.0900196 | 0.1624266 | 0.0346205 | 7.3 | 7.3 | 7.2 | 5.7 | 7.2 | 5.6 | 9.2 | 0.000455 | 0.0004450 | 0.000416 | 0.000056 | 0.000431 | 0.000036 | 0.000888 | 7.8 | 7.7 | 7.5 | 6.0 | 7.6 | 5.9 | 9.9 | 0.000429 | 0.0004250 | 0.000398 | 0.000031 | 0.000412 | 0.000027 | 0.000843 | 611 | 1881 | 6 | 4 |
| 146 | 36813 | -248 | -265 | -273 | -467 | -486 | -285 | -494 | -31 | 0.0174065 | 0.0533268 | 0.0998043 | 0.0168215 | 5.8 | 5.8 | 5.6 | 4.3 | 5.7 | 4.2 | 7.6 | 0.000545 | 0.0005410 | 0.000551 | 0.000233 | 0.000531 | 0.000223 | 0.000875 | 6.1 | 6.1 | 5.9 | 4.3 | 6.0 | 4.2 | 8.2 | 0.000578 | 0.0005700 | 0.000550 | 0.000196 | 0.000558 | 0.000183 | 0.000990 | 496 | 374 | 2 | 5 |
| 147 | 37468 | -128 | -127 | -127 | -118 | -248 | -133 | -247 | -8 | 0.0174065 | 0.0239726 | 0.0332681 | 0.0076371 | 6.8 | 6.8 | 6.7 | 5.5 | 6.7 | 5.5 | 8.2 | 0.000832 | 0.0008300 | 0.000828 | 0.000183 | 0.000809 | 0.000181 | 0.001495 | 7.3 | 7.2 | 7.1 | 5.8 | 7.2 | 5.8 | 8.8 | 0.000667 | 0.0006640 | 0.000650 | 0.000135 | 0.000648 | 0.000136 | 0.001213 | 255 | 486 | 3 | 3 |
| 148 | 37471 | -79 | -83 | -85 | -143 | -155 | -89 | -155 | -8 | 0.0235864 | 0.0459883 | 0.0841487 | 0.0153418 | 5.1 | 5.0 | 5.0 | 3.9 | 5.0 | 3.9 | 6.3 | 0.000778 | 0.0007650 | 0.000727 | 0.000413 | 0.000752 | 0.000391 | 0.001188 | 5.3 | 5.3 | 5.2 | 4.0 | 5.2 | 4.0 | 6.7 | 0.000527 | 0.0005190 | 0.000491 | 0.000218 | 0.000509 | 0.000203 | 0.000862 | 158 | 353 | 2 | 7 |
| 149 | 37510 | -176 | -187 | -192 | -284 | -344 | -201 | -349 | -21 | 0.0215264 | 0.0557730 | 0.0958904 | 0.0156451 | 6.4 | 6.4 | 6.2 | 4.6 | 6.3 | 4.5 | 8.5 | 0.000763 | 0.0007600 | 0.000757 | 0.000164 | 0.000741 | 0.000162 | 0.001369 | 6.9 | 6.8 | 6.8 | 4.6 | 6.7 | 4.5 | 9.4 | 0.000660 | 0.0006560 | 0.000646 | 0.000113 | 0.000639 | 0.000108 | 0.001225 | 352 | 351 | 2 | 7 |
| 150 | 37535 | -258 | -309 | -330 | -497 | -506 | -341 | -515 | -54 | 0.0586054 | 0.1658513 | 0.2798434 | 0.0961185 | 5.2 | 5.1 | 5.0 | 3.5 | 5.0 | 3.4 | 7.0 | 0.000936 | 0.0009320 | 0.000941 | 0.000380 | 0.000914 | 0.000370 | 0.001512 | 5.4 | 5.3 | 5.2 | 3.6 | 5.3 | 3.5 | 7.5 | 0.000711 | 0.0007060 | 0.000701 | 0.000197 | 0.000691 | 0.000191 | 0.001249 | 515 | 320 | 2 | 5 |
| 151 | 37539 | -182 | -161 | -151 | -15 | -349 | -160 | -336 | -2 | 0.0297662 | 0.0949119 | 0.1702544 | 0.0364909 | 7.3 | 7.2 | 7.2 | 5.5 | 7.2 | 5.4 | 9.3 | 0.000993 | 0.0009860 | 0.000983 | 0.000434 | 0.000968 | 0.000424 | 0.001588 | 7.7 | 7.7 | 7.5 | 5.6 | 7.6 | 5.5 | 10.1 | 0.000782 | 0.0007750 | 0.000758 | 0.000313 | 0.000760 | 0.000303 | 0.001281 | 363 | 1836 | 6 | 8 |
| 152 | 37541 | -254 | -182 | -158 | -13 | -466 | -167 | -431 | 0 | 0.1059841 | 0.2588063 | 0.3776908 | 0.1899520 | 8.4 | 8.3 | 8.1 | 6.7 | 8.3 | 6.6 | 10.4 | 0.000826 | 0.0008160 | 0.000790 | 0.000315 | 0.000800 | 0.000302 | 0.001372 | 9.1 | 9.0 | 8.9 | 7.2 | 9.0 | 7.0 | 11.3 | 0.000631 | 0.0006280 | 0.000614 | 0.000101 | 0.000612 | 0.000098 | 0.001177 | 507 | 500 | 3 | 6 |
| 153 | 37544 | -262 | -278 | -285 | -483 | -512 | -298 | -520 | -30 | 0.0153466 | 0.0533268 | 0.0919765 | 0.0152793 | 6.5 | 6.4 | 6.3 | 4.9 | 6.4 | 4.8 | 8.5 | 0.000590 | 0.0005860 | 0.000591 | 0.000041 | 0.000568 | 0.000032 | 0.001156 | 6.8 | 6.7 | 6.6 | 4.8 | 6.7 | 4.7 | 9.1 | 0.000751 | 0.0007460 | 0.000712 | 0.000222 | 0.000728 | 0.000209 | 0.001300 | 524 | 1478 | 5 | 4 |
| 154 | 37572 | -249 | -159 | -134 | -14 | -441 | -142 | -399 | 0 | 0.1574827 | 0.3370841 | 0.4598826 | 0.3056391 | 9.3 | 9.2 | 9.1 | 7.7 | 9.1 | 7.6 | 11.2 | 0.000816 | 0.0008080 | 0.000792 | 0.000560 | 0.000799 | 0.000544 | 0.001100 | 10.1 | 10.0 | 9.9 | 8.4 | 10.0 | 8.3 | 12.2 | 0.000513 | 0.0005090 | 0.000501 | 0.000189 | 0.000499 | 0.000181 | 0.000852 | 498 | 2225 | 7 | 4 |
| 155 | 37599 | -250 | -204 | -187 | -33 | -473 | -198 | -450 | 0 | 0.0544855 | 0.1634051 | 0.2524462 | 0.0842903 | 8.1 | 8.0 | 7.8 | 6.4 | 8.0 | 6.3 | 10.1 | 0.000722 | 0.0007160 | 0.000713 | 0.000281 | 0.000702 | 0.000273 | 0.001186 | 8.8 | 8.7 | 8.4 | 6.9 | 8.7 | 6.8 | 11.1 | 0.000603 | 0.0005990 | 0.000567 | 0.000078 | 0.000582 | 0.000074 | 0.001145 | 500 | 1940 | 6 | 3 |
| 156 | 37630 | -350 | -383 | -396 | -593 | -684 | -411 | -699 | -58 | 0.0359460 | 0.0973581 | 0.1389432 | 0.0343092 | 6.2 | 6.1 | 5.9 | 4.1 | 6.0 | 4.0 | 8.6 | 0.000937 | 0.0009250 | 0.000894 | 0.000549 | 0.000912 | 0.000525 | 0.001368 | 6.5 | 6.4 | 6.3 | 4.2 | 6.3 | 4.0 | 9.3 | 0.000718 | 0.0007140 | 0.000713 | 0.000198 | 0.000697 | 0.000191 | 0.001248 | 700 | 1096 | 4 | 5 |
| 157 | 37646 | -64 | -68 | -69 | -116 | -125 | -73 | -126 | -7 | 0.0256463 | 0.0533268 | 0.0958904 | 0.0193886 | 5.4 | 5.4 | 5.3 | 4.1 | 5.3 | 4.1 | 6.6 | 0.000901 | 0.0008880 | 0.000871 | 0.000543 | 0.000876 | 0.000522 | 0.001304 | 5.7 | 5.7 | 5.7 | 4.3 | 5.6 | 4.2 | 7.2 | 0.000660 | 0.0006530 | 0.000650 | 0.000203 | 0.000639 | 0.000192 | 0.001138 | 128 | 461 | 3 | 7 |
| 158 | 38958 | -163 | -193 | -206 | -313 | -321 | -213 | -326 | -31 | 0.0483057 | 0.1438356 | 0.2602740 | 0.0809633 | 4.5 | 4.5 | 4.4 | 3.2 | 4.4 | 3.1 | 6.0 | 0.001121 | 0.0011090 | 0.001101 | 0.000639 | 0.001094 | 0.000619 | 0.001647 | 4.7 | 4.6 | 4.5 | 3.2 | 4.6 | 3.2 | 6.3 | 0.000719 | 0.0007140 | 0.000698 | 0.000215 | 0.000698 | 0.000206 | 0.001239 | 326 | 725 | 4 | 5 |
| 159 | 38976 | -195 | -208 | -213 | -327 | -381 | -222 | -387 | -24 | 0.0215264 | 0.0557730 | 0.0958904 | 0.0164243 | 5.8 | 5.8 | 5.7 | 4.2 | 5.7 | 4.1 | 7.7 | 0.000985 | 0.0009760 | 0.000947 | 0.000534 | 0.000960 | 0.000520 | 0.001478 | 6.1 | 6.0 | 6.0 | 4.2 | 6.0 | 4.1 | 8.2 | 0.000714 | 0.0007100 | 0.000710 | 0.000175 | 0.000694 | 0.000165 | 0.001267 | 390 | 1758 | 5 | 4 |
| 160 | 38981 | -181 | -144 | -129 | -14 | -343 | -137 | -324 | 0 | 0.0586054 | 0.1682975 | 0.2837573 | 0.0987402 | 8.7 | 8.6 | 8.4 | 7.1 | 8.6 | 7.0 | 10.5 | 0.000753 | 0.0007490 | 0.000752 | 0.000307 | 0.000734 | 0.000294 | 0.001209 | 9.5 | 9.4 | 9.2 | 7.6 | 9.3 | 7.5 | 11.6 | 0.000529 | 0.0005290 | 0.000522 | 0.000128 | 0.000516 | 0.000128 | 0.000936 | 362 | 532 | 2 | 7 |
| 161 | 39013 | -258 | -252 | -249 | -192 | -499 | -261 | -488 | -7 | 0.0174065 | 0.0410959 | 0.0724070 | 0.0110212 | 6.8 | 6.8 | 6.5 | 5.3 | 6.7 | 5.2 | 8.7 | 0.000723 | 0.0007160 | 0.000687 | 0.000299 | 0.000702 | 0.000287 | 0.001172 | 7.3 | 7.2 | 7.0 | 5.6 | 7.2 | 5.5 | 9.4 | 0.000616 | 0.0006120 | 0.000608 | 0.000210 | 0.000599 | 0.000198 | 0.001038 | 515 | 2346 | 3 | 7 |
| 162 | 39014 | -262 | -303 | -319 | -404 | -513 | -330 | -523 | -52 | 0.0524256 | 0.1413894 | 0.2211350 | 0.0666788 | 5.7 | 5.6 | 5.5 | 4.0 | 5.6 | 3.8 | 7.7 | 0.001121 | 0.0011100 | 0.001102 | 0.000672 | 0.001095 | 0.000649 | 0.001610 | 6.0 | 5.9 | 5.7 | 4.1 | 5.9 | 3.9 | 8.3 | 0.000743 | 0.0007370 | 0.000733 | 0.000304 | 0.000722 | 0.000289 | 0.001201 | 523 | 737 | 3 | 6 |
| 163 | 39031 | -234 | -177 | -156 | -17 | -434 | -166 | -407 | 0 | 0.0833248 | 0.2172211 | 0.3307241 | 0.1415912 | 7.9 | 7.9 | 7.8 | 6.2 | 7.8 | 6.1 | 9.9 | 0.000974 | 0.0009630 | 0.000940 | 0.000490 | 0.000946 | 0.000471 | 0.001503 | 8.5 | 8.4 | 8.3 | 6.6 | 8.4 | 6.4 | 10.8 | 0.000669 | 0.0006650 | 0.000678 | 0.000141 | 0.000649 | 0.000135 | 0.001207 | 467 | 1814 | 5 | 8 |
| 164 | 39037 | -233 | -181 | -163 | -31 | -436 | -172 | -409 | 0 | 0.0750850 | 0.1927593 | 0.3033268 | 0.1191700 | 8.4 | 8.4 | 8.2 | 6.7 | 8.3 | 6.6 | 10.4 | 0.000800 | 0.0007750 | 0.000731 | 0.000477 | 0.000763 | 0.000441 | 0.001207 | 9.1 | 9.0 | 8.9 | 7.2 | 9.0 | 7.1 | 11.3 | 0.000633 | 0.0006300 | 0.000620 | 0.000108 | 0.000613 | 0.000101 | 0.001164 | 466 | 1283 | 4 | 6 |
| 165 | 39045 | -349 | -245 | -217 | -85 | -624 | -229 | -571 | 0 | 0.1389432 | 0.2954990 | 0.3816047 | 0.2288864 | 7.9 | 7.8 | 7.5 | 6.0 | 7.7 | 5.8 | 10.2 | 0.001000 | 0.0009880 | 0.000982 | 0.000569 | 0.000973 | 0.000549 | 0.001471 | 8.5 | 8.4 | 8.1 | 6.4 | 8.3 | 6.2 | 11.1 | 0.000643 | 0.0006400 | 0.000627 | 0.000123 | 0.000624 | 0.000115 | 0.001175 | 698 | 1062 | 5 | 5 |
| 166 | 39430 | -79 | -72 | -69 | -14 | -153 | -72 | -147 | -2 | 0.0318261 | 0.0777886 | 0.1389432 | 0.0284284 | 8.4 | 8.4 | 8.3 | 7.0 | 8.3 | 7.0 | 9.9 | 0.001265 | 0.0012540 | 0.001244 | 0.000879 | 0.001241 | 0.000856 | 0.001685 | 9.1 | 9.1 | 9.0 | 7.6 | 9.0 | 7.5 | 10.8 | 0.000785 | 0.0007790 | 0.000770 | 0.000403 | 0.000767 | 0.000395 | 0.001192 | 158 | 360 | 3 | 12 |
| 167 | 39432 | -234 | -220 | -215 | -183 | -448 | -225 | -433 | -5 | 0.0338861 | 0.0802348 | 0.1389432 | 0.0270069 | 7.2 | 7.1 | 6.9 | 5.4 | 7.1 | 5.2 | 9.4 | 0.001225 | 0.0012120 | 0.001171 | 0.000781 | 0.001197 | 0.000761 | 0.001715 | 7.8 | 7.7 | 7.4 | 5.7 | 7.6 | 5.5 | 10.2 | 0.000803 | 0.0007980 | 0.000801 | 0.000383 | 0.000784 | 0.000375 | 0.001247 | 467 | 1808 | 6 | 6 |
| 168 | 39454 | -262 | -263 | -263 | -235 | -510 | -276 | -515 | -23 | 0.0132866 | 0.0288650 | 0.0450098 | 0.0074693 | 6.7 | 6.6 | 6.4 | 5.1 | 6.5 | 5.0 | 8.6 | 0.000577 | 0.0005720 | 0.000560 | 0.000224 | 0.000560 | 0.000210 | 0.000947 | 7.1 | 7.0 | 6.8 | 5.2 | 6.9 | 5.1 | 9.3 | 0.000721 | 0.0007140 | 0.000704 | 0.000302 | 0.000701 | 0.000286 | 0.001161 | 525 | 728 | 4 | 6 |
| 169 | 41214 | -263 | -60 | -43 | -10 | -215 | -46 | -176 | 0 | 0.6477495 | 0.7676125 | 0.8317025 | 1.6953464 | 10.8 | 10.8 | 10.6 | 9.2 | 10.7 | 9.1 | 12.7 | 0.001341 | 0.0013350 | 0.001329 | 0.000765 | 0.001316 | 0.000751 | 0.001932 | 11.8 | 11.7 | 11.6 | 9.6 | 11.6 | 9.5 | 14.1 | 0.000660 | 0.0006570 | 0.000652 | 0.000108 | 0.000641 | 0.000107 | 0.001224 | 526 | 1157 | 3 | 9 |
| 170 | 4151F | -254 | -232 | -223 | -21 | -490 | -235 | -473 | -2 | 0.0235864 | 0.0680039 | 0.1232877 | 0.0225156 | 7.7 | 7.7 | 7.6 | 6.0 | 7.6 | 5.9 | 9.6 | 0.000524 | 0.0005180 | 0.000512 | 0.000285 | 0.000511 | 0.000278 | 0.000784 | 8.4 | 8.3 | 8.3 | 6.4 | 8.3 | 6.2 | 10.6 | 0.000435 | 0.0004300 | 0.000401 | 0.000060 | 0.000418 | 0.000052 | 0.000824 | 508 | 1989 | 6 | 6 |
| 171 | 41795 | -240 | -259 | -267 | -391 | -469 | -279 | -477 | -32 | 0.0215264 | 0.0680039 | 0.1193738 | 0.0211856 | 7.0 | 6.9 | 6.7 | 5.3 | 6.8 | 5.2 | 8.9 | 0.000633 | 0.0006270 | 0.000589 | 0.000222 | 0.000613 | 0.000209 | 0.001069 | 7.3 | 7.2 | 6.9 | 5.3 | 7.1 | 5.1 | 9.6 | 0.000914 | 0.0009060 | 0.000877 | 0.000492 | 0.000893 | 0.000474 | 0.001363 | 479 | 2576 | 7 | 2 |
| 172 | 42558 | -84 | -73 | -67 | -13 | -162 | -71 | -154 | 0 | 0.0400659 | 0.1095890 | 0.2054795 | 0.0532639 | 9.4 | 9.4 | 9.3 | 8.0 | 9.3 | 8.0 | 10.8 | 0.000788 | 0.0007800 | 0.000767 | 0.000448 | 0.000768 | 0.000430 | 0.001162 | 10.3 | 10.2 | 10.2 | 8.7 | 10.2 | 8.7 | 11.9 | 0.000655 | 0.0006510 | 0.000646 | 0.000229 | 0.000638 | 0.000221 | 0.001097 | 169 | 1861 | 3 | 7 |
| 173 | 43388 | -154 | -164 | -168 | -207 | -299 | -174 | -304 | -20 | 0.0277063 | 0.0728963 | 0.1037182 | 0.0200794 | 8.4 | 8.4 | 8.3 | 6.7 | 8.3 | 6.6 | 10.2 | 0.001015 | 0.0010010 | 0.000998 | 0.000702 | 0.000990 | 0.000675 | 0.001379 | 9.3 | 9.2 | 9.3 | 7.4 | 9.2 | 7.3 | 11.3 | 0.000559 | 0.0005500 | 0.000532 | 0.000291 | 0.000541 | 0.000274 | 0.000864 | 307 | 1350 | 2 | 9 |
| 174 | 43929 | -244 | -311 | -337 | -476 | -482 | -346 | -488 | -63 | 0.0833248 | 0.2294521 | 0.3737769 | 0.1713061 | 4.2 | 4.1 | 4.0 | 2.6 | 4.1 | 2.5 | 6.0 | 0.001095 | 0.0010810 | 0.001065 | 0.000630 | 0.001066 | 0.000606 | 0.001607 | 4.3 | 4.2 | 4.2 | 2.6 | 4.2 | 2.4 | 6.3 | 0.000662 | 0.0006570 | 0.000643 | 0.000210 | 0.000642 | 0.000196 | 0.001127 | 488 | 765 | 3 | 4 |
| 175 | 4466C | -272 | -380 | -415 | -528 | -538 | -424 | -543 | -104 | 0.1492430 | 0.3566536 | 0.5225049 | 0.3672776 | 3.2 | 3.2 | 3.1 | 1.4 | 3.1 | 1.3 | 5.2 | 0.001239 | 0.0012300 | 0.001208 | 0.000644 | 0.001211 | 0.000625 | 0.001863 | 3.3 | 3.2 | 3.1 | 1.5 | 3.2 | 1.4 | 5.4 | 0.000681 | 0.0006770 | 0.000669 | 0.000199 | 0.000662 | 0.000194 | 0.001184 | 543 | 2220 | 8 | 6 |
| 176 | 46127 | -110 | -100 | -95 | -14 | -213 | -101 | -206 | -2 | 0.0235864 | 0.0680039 | 0.1389432 | 0.0254449 | 8.1 | 8.1 | 8.0 | 6.8 | 8.0 | 6.8 | 9.4 | 0.000481 | 0.0004620 | 0.000436 | 0.000198 | 0.000452 | 0.000170 | 0.000827 | 8.9 | 8.9 | 8.8 | 7.5 | 8.8 | 7.4 | 10.5 | 0.000551 | 0.0005500 | 0.000565 | 0.000077 | 0.000535 | 0.000071 | 0.001030 | 220 | 942 | 3 | 7 |
| 177 | 46621 | -112 | -88 | -77 | -12 | -213 | -82 | -201 | 0 | 0.0606654 | 0.1805284 | 0.3072407 | 0.1160441 | 9.4 | 9.3 | 9.3 | 7.9 | 9.3 | 7.8 | 11.0 | 0.000855 | 0.0008380 | 0.000808 | 0.000496 | 0.000825 | 0.000469 | 0.001279 | 10.1 | 10.1 | 10.1 | 8.4 | 10.0 | 8.3 | 12.0 | 0.000665 | 0.0006610 | 0.000621 | 0.000124 | 0.000644 | 0.000123 | 0.001219 | 225 | 1424 | 3 | 8 |
| 178 | 46766 | -239 | -278 | -293 | -446 | -469 | -303 | -478 | -49 | 0.0544855 | 0.1462818 | 0.2250489 | 0.0699637 | 6.1 | 6.0 | 5.9 | 4.2 | 5.9 | 4.1 | 8.2 | 0.001113 | 0.0011050 | 0.001089 | 0.000613 | 0.001088 | 0.000597 | 0.001645 | 6.5 | 6.4 | 6.2 | 4.4 | 6.3 | 4.3 | 8.9 | 0.000815 | 0.0008090 | 0.000823 | 0.000386 | 0.000795 | 0.000375 | 0.001266 | 478 | 750 | 2 | 6 |
| 179 | 47B58 | -227 | -222 | -220 | -151 | -441 | -231 | -432 | -6 | 0.0132866 | 0.0264188 | 0.0410959 | 0.0076000 | 7.3 | 7.2 | 7.1 | 5.8 | 7.2 | 5.7 | 8.9 | 0.000543 | 0.0005380 | 0.000534 | 0.000342 | 0.000532 | 0.000334 | 0.000762 | 7.9 | 7.8 | 7.7 | 6.3 | 7.8 | 6.2 | 9.7 | 0.000448 | 0.0004450 | 0.000437 | 0.000218 | 0.000438 | 0.000209 | 0.000688 | 454 | 1380 | 3 | 4 |
| 180 | 50218 | -254 | -217 | -201 | -64 | -486 | -212 | -463 | 0 | 0.0421259 | 0.1242661 | 0.2133072 | 0.0562514 | 8.3 | 8.2 | 8.1 | 6.7 | 8.2 | 6.6 | 10.0 | 0.000285 | 0.0002820 | 0.000286 | 0.000008 | 0.000273 | 0.000004 | 0.000574 | 8.9 | 8.9 | 8.7 | 7.1 | 8.8 | 7.0 | 11.0 | 0.000640 | 0.0006360 | 0.000625 | 0.000123 | 0.000619 | 0.000116 | 0.001171 | 509 | 1507 | 5 | 3 |
| 181 | 50473 | -221 | -122 | -99 | -12 | -363 | -105 | -321 | 0 | 0.2357606 | 0.4300391 | 0.5499022 | 0.4770959 | 9.4 | 9.3 | 9.1 | 8.0 | 9.3 | 7.8 | 11.2 | 0.000963 | 0.0009440 | 0.000918 | 0.000582 | 0.000930 | 0.000555 | 0.001409 | 10.2 | 10.1 | 9.9 | 8.6 | 10.0 | 8.5 | 12.1 | 0.000606 | 0.0006060 | 0.000614 | 0.000080 | 0.000589 | 0.000080 | 0.001137 | 442 | 371 | 3 | 6 |
| 182 | 50E01 | -230 | -207 | -198 | -84 | -441 | -209 | -425 | -2 | 0.0297662 | 0.0900196 | 0.1389432 | 0.0302685 | 7.9 | 7.9 | 7.7 | 6.1 | 7.8 | 6.0 | 10.0 | 0.000728 | 0.0007180 | 0.000699 | 0.000287 | 0.000704 | 0.000271 | 0.001204 | 8.6 | 8.5 | 8.5 | 6.4 | 8.5 | 6.3 | 11.1 | 0.000448 | 0.0004440 | 0.000446 | 0.000072 | 0.000432 | 0.000063 | 0.000838 | 459 | 2534 | 4 | 6 |
| 183 | 51A77 | -110 | -125 | -132 | -208 | -217 | -137 | -221 | -19 | 0.0359460 | 0.1046967 | 0.1976517 | 0.0478552 | 5.9 | 5.8 | 5.8 | 4.4 | 5.8 | 4.3 | 7.4 | 0.001141 | 0.0011280 | 0.001101 | 0.000667 | 0.001113 | 0.000644 | 0.001663 | 6.3 | 6.3 | 6.3 | 4.7 | 6.2 | 4.6 | 8.1 | 0.000684 | 0.0006750 | 0.000656 | 0.000312 | 0.000662 | 0.000298 | 0.001091 | 221 | 1694 | 3 | 7 |
| 184 | 52462 | -365 | -422 | -447 | -711 | -717 | -463 | -729 | -62 | 0.0380060 | 0.1218200 | 0.2250489 | 0.0606574 | 5.1 | 5.0 | 4.7 | 3.4 | 5.0 | 3.3 | 7.2 | 0.000515 | 0.0005060 | 0.000511 | 0.000151 | 0.000494 | 0.000135 | 0.000910 | 5.4 | 5.3 | 5.1 | 3.4 | 5.3 | 3.3 | 7.8 | 0.000718 | 0.0007140 | 0.000711 | 0.000241 | 0.000699 | 0.000234 | 0.001213 | 730 | 71 | 2 | 9 |
| 185 | 52858 | -230 | -225 | -223 | -206 | -445 | -233 | -439 | -11 | 0.0174065 | 0.0484344 | 0.0880626 | 0.0131864 | 7.2 | 7.1 | 6.8 | 5.2 | 7.0 | 5.0 | 9.5 | 0.000995 | 0.0009910 | 0.001008 | 0.000419 | 0.000972 | 0.000412 | 0.001585 | 7.7 | 7.6 | 7.3 | 5.2 | 7.5 | 5.1 | 10.5 | 0.000659 | 0.0006550 | 0.000643 | 0.000109 | 0.000638 | 0.000104 | 0.001221 | 459 | 0 | 1 | 4 |
| 186 | 53002 | -126 | -123 | -121 | -28 | -245 | -127 | -241 | -5 | 0.0174065 | 0.0313112 | 0.0489237 | 0.0087231 | 7.3 | 7.3 | 7.2 | 5.9 | 7.2 | 5.8 | 8.8 | 0.000659 | 0.0006470 | 0.000605 | 0.000387 | 0.000637 | 0.000366 | 0.000971 | 7.9 | 7.8 | 7.7 | 6.2 | 7.8 | 6.2 | 9.6 | 0.000583 | 0.0005760 | 0.000575 | 0.000280 | 0.000567 | 0.000265 | 0.000913 | 252 | 2549 | 4 | 1 |
| 187 | 54516 | -313 | -236 | -206 | -13 | -587 | -219 | -552 | 0 | 0.0709651 | 0.2098826 | 0.3385519 | 0.1412694 | 8.4 | 8.3 | 8.2 | 7.0 | 8.3 | 6.9 | 10.0 | 0.000294 | 0.0002850 | 0.000276 | 0.000081 | 0.000278 | 0.000066 | 0.000537 | 9.1 | 9.0 | 8.9 | 7.5 | 9.0 | 7.4 | 11.0 | 0.000500 | 0.0004960 | 0.000470 | 0.000036 | 0.000481 | 0.000028 | 0.000978 | 626 | 1555 | 3 | 6 |
| 188 | 55860 | -80 | -72 | -68 | -14 | -153 | -72 | -147 | -1 | 0.0318261 | 0.0802348 | 0.1467710 | 0.0307370 | 8.9 | 8.8 | 8.9 | 7.4 | 8.8 | 7.4 | 10.5 | 0.000822 | 0.0008210 | 0.000827 | 0.000148 | 0.000799 | 0.000140 | 0.001500 | 9.6 | 9.6 | 9.4 | 7.7 | 9.5 | 7.6 | 11.6 | 0.000659 | 0.0006550 | 0.000648 | 0.000111 | 0.000638 | 0.000102 | 0.001223 | 159 | 1858 | 6 | 5 |
| 189 | 56111 | -55 | -58 | -60 | -97 | -108 | -63 | -108 | -6 | 0.0277063 | 0.0606654 | 0.1076321 | 0.0221380 | 5.7 | 5.7 | 5.6 | 4.3 | 5.6 | 4.3 | 7.1 | 0.000901 | 0.0008980 | 0.000930 | 0.000217 | 0.000876 | 0.000207 | 0.001601 | 6.1 | 6.1 | 6.1 | 4.5 | 6.1 | 4.5 | 7.7 | 0.000649 | 0.0006460 | 0.000630 | 0.000098 | 0.000628 | 0.000094 | 0.001210 | 110 | 614 | 2 | 6 |
| 190 | 56141 | -252 | -350 | -382 | -490 | -498 | -390 | -503 | -94 | 0.1451231 | 0.3517613 | 0.5146771 | 0.3529079 | 3.7 | 3.7 | 3.6 | 1.8 | 3.6 | 1.7 | 5.8 | 0.001173 | 0.0011650 | 0.001143 | 0.000577 | 0.001145 | 0.000558 | 0.001797 | 3.9 | 3.9 | 3.8 | 1.8 | 3.8 | 1.8 | 6.2 | 0.000652 | 0.0006480 | 0.000644 | 0.000112 | 0.000631 | 0.000105 | 0.001201 | 503 | 3838 | 8 | 5 |
| 191 | 58646 | -236 | -238 | -239 | -296 | -459 | -250 | -460 | -16 | 0.0112267 | 0.0264188 | 0.0489237 | 0.0079669 | 7.0 | 6.9 | 6.8 | 5.5 | 6.9 | 5.4 | 8.7 | 0.000529 | 0.0005120 | 0.000463 | 0.000199 | 0.000500 | 0.000172 | 0.000919 | 7.4 | 7.3 | 7.2 | 5.6 | 7.3 | 5.5 | 9.4 | 0.000710 | 0.0007070 | 0.000689 | 0.000220 | 0.000691 | 0.000210 | 0.001212 | 472 | 2199 | 7 | 6 |
| 192 | 58709 | -156 | -162 | -164 | -278 | -305 | -172 | -307 | -16 | 0.0174065 | 0.0337573 | 0.0567515 | 0.0094220 | 6.0 | 6.0 | 6.0 | 4.7 | 6.0 | 4.6 | 7.5 | 0.000621 | 0.0006120 | 0.000594 | 0.000309 | 0.000601 | 0.000295 | 0.000963 | 6.4 | 6.4 | 6.3 | 4.9 | 6.4 | 4.9 | 8.1 | 0.000514 | 0.0005060 | 0.000487 | 0.000180 | 0.000494 | 0.000163 | 0.000883 | 312 | 351 | 2 | 8 |
| 193 | 59147 | -248 | -223 | -214 | -186 | -472 | -225 | -451 | -3 | 0.0462458 | 0.1169276 | 0.1663405 | 0.0446195 | 8.1 | 8.0 | 7.9 | 6.2 | 8.0 | 6.1 | 10.2 | 0.001053 | 0.0010420 | 0.001011 | 0.000577 | 0.001026 | 0.000560 | 0.001569 | 8.8 | 8.7 | 8.5 | 6.7 | 8.7 | 6.6 | 11.2 | 0.000597 | 0.0005940 | 0.000606 | 0.000071 | 0.000578 | 0.000069 | 0.001132 | 495 | 349 | 2 | 7 |
| 194 | 59843 | -148 | -160 | -166 | -282 | -290 | -173 | -294 | -18 | 0.0194665 | 0.0631115 | 0.1272016 | 0.0211890 | 5.9 | 5.8 | 5.7 | 4.6 | 5.8 | 4.6 | 7.2 | 0.000562 | 0.0005530 | 0.000544 | 0.000344 | 0.000545 | 0.000328 | 0.000815 | 6.0 | 6.0 | 6.0 | 4.6 | 6.0 | 4.5 | 7.6 | 0.000703 | 0.0006930 | 0.000680 | 0.000389 | 0.000682 | 0.000374 | 0.001056 | 296 | 1467 | 5 | 8 |
| 195 | 60122 | -172 | -163 | -159 | -63 | -333 | -167 | -324 | -4 | 0.0235864 | 0.0557730 | 0.0880626 | 0.0147853 | 7.5 | 7.4 | 7.4 | 6.0 | 7.4 | 5.9 | 9.1 | 0.000835 | 0.0008260 | 0.000803 | 0.000564 | 0.000816 | 0.000547 | 0.001138 | 8.0 | 8.0 | 7.8 | 6.4 | 7.9 | 6.3 | 9.9 | 0.000582 | 0.0005790 | 0.000580 | 0.000202 | 0.000566 | 0.000195 | 0.000972 | 345 | 70 | 2 | 6 |
| 196 | 62159 | -240 | -183 | -160 | -12 | -452 | -170 | -425 | 0 | 0.0689051 | 0.2000978 | 0.3268102 | 0.1329510 | 8.5 | 8.4 | 8.2 | 6.6 | 8.4 | 6.5 | 10.7 | 0.000751 | 0.0007440 | 0.000727 | 0.000198 | 0.000726 | 0.000189 | 0.001331 | 9.2 | 9.1 | 9.0 | 6.8 | 9.0 | 6.7 | 11.8 | 0.000660 | 0.0006570 | 0.000644 | 0.000112 | 0.000640 | 0.000103 | 0.001218 | 481 | 2567 | 7 | 1 |
| 197 | 62447 | -186 | -160 | -149 | -36 | -356 | -157 | -339 | 0 | 0.0400659 | 0.1169276 | 0.1976517 | 0.0508485 | 8.1 | 8.1 | 7.9 | 6.3 | 8.0 | 6.1 | 10.3 | 0.000807 | 0.0008040 | 0.000802 | 0.000245 | 0.000785 | 0.000239 | 0.001382 | 8.7 | 8.7 | 8.5 | 6.4 | 8.6 | 6.3 | 11.3 | 0.000660 | 0.0006560 | 0.000646 | 0.000107 | 0.000639 | 0.000098 | 0.001220 | 372 | 786 | 3 | 6 |
| 198 | 63653 | -186 | -121 | -102 | -14 | -329 | -109 | -299 | 0 | 0.1513029 | 0.3224070 | 0.4442270 | 0.2849895 | 8.1 | 7.9 | 7.8 | 6.3 | 7.9 | 6.2 | 10.1 | 0.001440 | 0.0014270 | 0.001397 | 0.001018 | 0.001413 | 0.000999 | 0.001912 | 8.6 | 8.5 | 8.2 | 6.7 | 8.4 | 6.5 | 11.0 | 0.000997 | 0.0009880 | 0.000973 | 0.000579 | 0.000974 | 0.000566 | 0.001449 | 371 | 752 | 3 | 6 |
| 199 | 64939 | -262 | -266 | -267 | -317 | -510 | -279 | -516 | -27 | 0.0194665 | 0.0508806 | 0.0880626 | 0.0133410 | 7.0 | 6.9 | 6.7 | 5.0 | 6.8 | 4.8 | 9.4 | 0.000872 | 0.0008690 | 0.000880 | 0.000231 | 0.000849 | 0.000221 | 0.001527 | 7.5 | 7.4 | 7.2 | 5.1 | 7.3 | 4.9 | 10.3 | 0.000660 | 0.0006570 | 0.000675 | 0.000109 | 0.000640 | 0.000104 | 0.001220 | 525 | 1478 | 5 | 6 |
| 200 | 65143 | -356 | -513 | -556 | -692 | -705 | -566 | -711 | -177 | 0.2089814 | 0.4178082 | 0.5577299 | 0.4643832 | 4.5 | 4.5 | 4.5 | 2.3 | 4.4 | 2.2 | 7.0 | 0.001182 | 0.0011670 | 0.001133 | 0.000677 | 0.001150 | 0.000648 | 0.001739 | 4.8 | 4.7 | 4.6 | 2.4 | 4.6 | 2.3 | 7.5 | 0.000712 | 0.0007070 | 0.000704 | 0.000277 | 0.000693 | 0.000265 | 0.001171 | 711 | 1380 | 5 | 3 |
| 201 | 66F7D | -312 | -256 | -236 | -141 | -587 | -249 | -557 | -1 | 0.0606654 | 0.1658513 | 0.2446184 | 0.0852901 | 7.7 | 7.6 | 7.3 | 5.9 | 7.5 | 5.8 | 9.9 | 0.000790 | 0.0007800 | 0.000758 | 0.000380 | 0.000767 | 0.000361 | 0.001234 | 8.3 | 8.2 | 7.9 | 6.3 | 8.1 | 6.2 | 10.8 | 0.000631 | 0.0006260 | 0.000627 | 0.000201 | 0.000612 | 0.000188 | 0.001079 | 623 | 3051 | 6 | 5 |
| 202 | 70486 | -243 | -225 | -218 | -92 | -469 | -229 | -453 | -3 | 0.0256463 | 0.0704501 | 0.1115460 | 0.0203340 | 7.0 | 6.9 | 6.6 | 5.3 | 6.8 | 5.2 | 9.0 | 0.000859 | 0.0008500 | 0.000834 | 0.000412 | 0.000835 | 0.000397 | 0.001340 | 7.4 | 7.3 | 7.1 | 5.5 | 7.2 | 5.4 | 9.7 | 0.000721 | 0.0007150 | 0.000687 | 0.000309 | 0.000702 | 0.000300 | 0.001151 | 486 | 380 | 2 | 9 |
| 203 | 70725 | -170 | -159 | -155 | -119 | -328 | -163 | -319 | -3 | 0.0256463 | 0.0728963 | 0.1076321 | 0.0199708 | 7.4 | 7.3 | 7.2 | 5.8 | 7.3 | 5.7 | 9.3 | 0.001130 | 0.0011190 | 0.001100 | 0.000734 | 0.001107 | 0.000713 | 0.001563 | 8.0 | 7.9 | 7.6 | 6.2 | 7.8 | 6.1 | 10.1 | 0.000877 | 0.0008690 | 0.000853 | 0.000475 | 0.000857 | 0.000465 | 0.001305 | 341 | 1693 | 3 | 2 |
| 204 | 71112 | -200 | -231 | -245 | -381 | -394 | -254 | -401 | -35 | 0.0380060 | 0.1120352 | 0.2172211 | 0.0555361 | 4.8 | 4.7 | 4.7 | 3.5 | 4.7 | 3.4 | 6.3 | 0.000639 | 0.0006290 | 0.000610 | 0.000189 | 0.000614 | 0.000173 | 0.001120 | 4.9 | 4.9 | 4.8 | 3.4 | 4.8 | 3.3 | 6.7 | 0.000829 | 0.0008220 | 0.000819 | 0.000351 | 0.000806 | 0.000340 | 0.001333 | 401 | 1105 | 3 | 6 |
| 205 | 71353 | -254 | -246 | -243 | -185 | -492 | -255 | -484 | -7 | 0.0112267 | 0.0313112 | 0.0450098 | 0.0082118 | 6.9 | 6.8 | 6.6 | 5.3 | 6.8 | 5.2 | 8.7 | 0.000569 | 0.0005560 | 0.000520 | 0.000307 | 0.000547 | 0.000287 | 0.000875 | 7.5 | 7.4 | 7.2 | 5.8 | 7.4 | 5.7 | 9.5 | 0.000580 | 0.0005750 | 0.000572 | 0.000196 | 0.000563 | 0.000186 | 0.000981 | 507 | 178 | 2 | 7 |
| 206 | 72320 | -182 | -115 | -94 | -11 | -326 | -101 | -296 | 0 | 0.1430631 | 0.3321918 | 0.4755382 | 0.3108799 | 9.2 | 9.1 | 9.0 | 7.3 | 9.0 | 7.2 | 11.3 | 0.001060 | 0.0010530 | 0.001055 | 0.000511 | 0.001035 | 0.000499 | 0.001638 | 9.9 | 9.8 | 9.7 | 7.6 | 9.7 | 7.4 | 12.5 | 0.000659 | 0.0006570 | 0.000669 | 0.000106 | 0.000639 | 0.000110 | 0.001227 | 364 | 2200 | 8 | 8 |
| 207 | 72507 | -230 | -276 | -296 | -437 | -452 | -306 | -459 | -45 | 0.0503656 | 0.1560665 | 0.2876712 | 0.0958386 | 4.4 | 4.3 | 4.2 | 2.7 | 4.3 | 2.7 | 6.2 | 0.000746 | 0.0007290 | 0.000689 | 0.000242 | 0.000711 | 0.000212 | 0.001311 | 4.6 | 4.5 | 4.5 | 2.7 | 4.4 | 2.6 | 6.6 | 0.000685 | 0.0006820 | 0.000688 | 0.000148 | 0.000665 | 0.000145 | 0.001237 | 459 | 359 | 2 | 7 |
| 208 | 73050 | -246 | -216 | -202 | -21 | -472 | -214 | -455 | -1 | 0.0297662 | 0.0973581 | 0.1741683 | 0.0401692 | 7.9 | 7.8 | 7.8 | 6.4 | 7.8 | 6.3 | 9.6 | 0.000417 | 0.0004130 | 0.000424 | 0.000139 | 0.000404 | 0.000129 | 0.000711 | 8.5 | 8.5 | 8.4 | 6.8 | 8.4 | 6.7 | 10.5 | 0.000453 | 0.0004490 | 0.000438 | 0.000078 | 0.000436 | 0.000071 | 0.000843 | 492 | 367 | 2 | 4 |
| 209 | 73655 | -58 | -60 | -62 | -100 | -113 | -65 | -113 | -6 | 0.0277063 | 0.0508806 | 0.0880626 | 0.0187506 | 5.1 | 5.1 | 5.1 | 3.9 | 5.0 | 3.8 | 6.3 | 0.001185 | 0.0011770 | 0.001170 | 0.000533 | 0.001156 | 0.000516 | 0.001875 | 5.2 | 5.2 | 5.2 | 3.9 | 5.2 | 3.8 | 6.7 | 0.000858 | 0.0008460 | 0.000797 | 0.000328 | 0.000828 | 0.000306 | 0.001430 | 115 | 74 | 2 | 7 |
| 210 | 7366A | -174 | -143 | -129 | -16 | -332 | -137 | -315 | 0 | 0.0503656 | 0.1413894 | 0.2563601 | 0.0783929 | 8.6 | 8.6 | 8.4 | 7.2 | 8.5 | 7.1 | 10.3 | 0.000726 | 0.0007180 | 0.000708 | 0.000486 | 0.000710 | 0.000473 | 0.000996 | 9.6 | 9.6 | 9.4 | 8.0 | 9.5 | 7.9 | 11.5 | 0.000663 | 0.0006570 | 0.000646 | 0.000322 | 0.000646 | 0.000311 | 0.001025 | 349 | 1554 | 4 | 4 |
| 211 | 74770 | -132 | -121 | -116 | -20 | -254 | -122 | -245 | -2 | 0.0297662 | 0.0777886 | 0.1311155 | 0.0251054 | 8.4 | 8.4 | 8.3 | 7.0 | 8.3 | 7.0 | 10.0 | 0.000896 | 0.0008890 | 0.000854 | 0.000371 | 0.000872 | 0.000359 | 0.001438 | 9.2 | 9.1 | 9.0 | 7.6 | 9.1 | 7.5 | 10.9 | 0.000463 | 0.0004640 | 0.000472 | -0.000006 | 0.000450 | -0.000004 | 0.000926 | 264 | 1491 | 4 | 7 |
| 212 | 7582C | -232 | -275 | -291 | -428 | -457 | -301 | -465 | -45 | 0.0503656 | 0.1560665 | 0.2485323 | 0.0811822 | 5.9 | 5.8 | 5.7 | 3.7 | 5.8 | 3.6 | 8.4 | 0.001027 | 0.0010230 | 0.001007 | 0.000426 | 0.001004 | 0.000416 | 0.001642 | 6.3 | 6.2 | 5.8 | 3.7 | 6.1 | 3.5 | 9.3 | 0.000659 | 0.0006560 | 0.000665 | 0.000113 | 0.000639 | 0.000109 | 0.001221 | 465 | 942 | 3 | 4 |
| 213 | 7634C | -78 | -83 | -85 | -143 | -153 | -88 | -154 | -8 | 0.0235864 | 0.0508806 | 0.0841487 | 0.0157137 | 6.2 | 6.2 | 6.2 | 4.8 | 6.2 | 4.7 | 7.8 | 0.000962 | 0.0009580 | 0.000946 | 0.000289 | 0.000937 | 0.000278 | 0.001651 | 6.7 | 6.6 | 6.5 | 4.7 | 6.6 | 4.7 | 8.7 | 0.000659 | 0.0006560 | 0.000642 | 0.000111 | 0.000639 | 0.000108 | 0.001221 | 157 | 695 | 2 | 5 |
| 214 | 77067 | -184 | -200 | -207 | -344 | -359 | -215 | -365 | -25 | 0.0256463 | 0.0728963 | 0.1311155 | 0.0233964 | 6.0 | 6.0 | 5.9 | 4.4 | 5.9 | 4.4 | 7.7 | 0.000742 | 0.0007320 | 0.000712 | 0.000377 | 0.000720 | 0.000360 | 0.001141 | 6.4 | 6.3 | 6.2 | 4.6 | 6.3 | 4.5 | 8.3 | 0.000671 | 0.0006670 | 0.000649 | 0.000136 | 0.000651 | 0.000131 | 0.001219 | 367 | 2565 | 8 | 6 |
| 215 | 77808 | -352 | -204 | -171 | -32 | -581 | -181 | -512 | 0 | 0.2357606 | 0.4129159 | 0.5146771 | 0.4356424 | 8.3 | 8.2 | 8.1 | 6.2 | 8.1 | 6.0 | 10.9 | 0.001050 | 0.0010370 | 0.001025 | 0.000651 | 0.001024 | 0.000629 | 0.001495 | 9.0 | 8.8 | 8.6 | 6.4 | 8.8 | 6.2 | 12.0 | 0.000660 | 0.0006560 | 0.000650 | 0.000109 | 0.000639 | 0.000099 | 0.001219 | 705 | 1496 | 3 | 8 |
| 216 | 78089 | -310 | -323 | -329 | -417 | -604 | -343 | -615 | -39 | 0.0235864 | 0.0557730 | 0.0919765 | 0.0160620 | 6.7 | 6.7 | 6.4 | 4.8 | 6.6 | 4.7 | 8.9 | 0.000739 | 0.0007310 | 0.000747 | 0.000326 | 0.000717 | 0.000312 | 0.001181 | 7.3 | 7.2 | 6.9 | 5.1 | 7.1 | 5.0 | 9.8 | 0.000612 | 0.0006090 | 0.000592 | 0.000094 | 0.000592 | 0.000092 | 0.001140 | 621 | 1081 | 4 | 5 |
| 217 | 78483 | -300 | -282 | -274 | -106 | -580 | -288 | -562 | -4 | 0.0194665 | 0.0582192 | 0.0919765 | 0.0162310 | 6.9 | 6.8 | 6.6 | 5.2 | 6.7 | 5.1 | 8.9 | 0.000630 | 0.0006200 | 0.000611 | 0.000183 | 0.000605 | 0.000168 | 0.001119 | 7.3 | 7.2 | 7.1 | 5.4 | 7.2 | 5.3 | 9.6 | 0.000516 | 0.0005130 | 0.000511 | 0.000097 | 0.000500 | 0.000087 | 0.000943 | 600 | 1565 | 5 | 8 |
| 218 | 78624 | -259 | -226 | -215 | -166 | -492 | -226 | -469 | -1 | 0.0483057 | 0.1291585 | 0.1780822 | 0.0531803 | 7.0 | 6.8 | 6.6 | 5.0 | 6.8 | 4.8 | 9.4 | 0.001239 | 0.0012250 | 0.001186 | 0.000862 | 0.001212 | 0.000838 | 0.001666 | 7.3 | 7.2 | 6.6 | 5.0 | 7.1 | 4.9 | 10.1 | 0.001070 | 0.0010610 | 0.001015 | 0.000652 | 0.001047 | 0.000634 | 0.001522 | 518 | 2182 | 6 | 6 |
| 219 | 78746 | -161 | -133 | -121 | -16 | -307 | -129 | -291 | 0 | 0.0483057 | 0.1389432 | 0.2446184 | 0.0731348 | 8.7 | 8.6 | 8.5 | 6.9 | 8.6 | 6.8 | 10.6 | 0.000788 | 0.0007720 | 0.000735 | 0.000418 | 0.000759 | 0.000393 | 0.001211 | 9.3 | 9.3 | 9.1 | 7.1 | 9.2 | 7.0 | 11.8 | 0.000659 | 0.0006555 | 0.000631 | 0.000110 | 0.000639 | 0.000098 | 0.001217 | 322 | 1834 | 5 | 7 |
| 220 | 79812 | -260 | -258 | -256 | -288 | -505 | -269 | -498 | -11 | 0.0174065 | 0.0362035 | 0.0645793 | 0.0101464 | 6.6 | 6.5 | 6.2 | 4.8 | 6.4 | 4.7 | 8.6 | 0.000918 | 0.0009080 | 0.000893 | 0.000519 | 0.000895 | 0.000501 | 0.001353 | 6.9 | 6.8 | 6.5 | 4.9 | 6.7 | 4.8 | 9.3 | 0.000781 | 0.0007760 | 0.000769 | 0.000402 | 0.000764 | 0.000388 | 0.001181 | 521 | 350 | 2 | 8 |
| 221 | 79839 | -59 | -58 | -58 | -21 | -115 | -61 | -113 | -4 | 0.0256463 | 0.0386497 | 0.0450098 | 0.0133761 | 7.5 | 7.5 | 7.6 | 6.2 | 7.5 | 6.2 | 8.9 | 0.000645 | 0.0006400 | 0.000630 | 0.000371 | 0.000631 | 0.000361 | 0.000937 | 7.9 | 7.9 | 8.0 | 6.4 | 7.9 | 6.3 | 9.5 | 0.000690 | 0.0006860 | 0.000703 | 0.000141 | 0.000668 | 0.000131 | 0.001253 | 118 | 414 | 2 | 4 |
| 222 | 79871 | -68 | -67 | -66 | -33 | -133 | -70 | -131 | -4 | 0.0235864 | 0.0386497 | 0.0489237 | 0.0122806 | 7.7 | 7.7 | 7.6 | 6.5 | 7.6 | 6.5 | 8.9 | 0.000787 | 0.0007790 | 0.000772 | 0.000502 | 0.000769 | 0.000489 | 0.001098 | 8.4 | 8.3 | 8.3 | 7.0 | 8.3 | 7.0 | 9.8 | 0.000649 | 0.0006450 | 0.000625 | 0.000336 | 0.000634 | 0.000327 | 0.000975 | 137 | 349 | 2 | 7 |
| 223 | 79968 | -46 | -45 | -44 | -14 | -90 | -46 | -88 | -3 | 0.0277063 | 0.0484344 | 0.0802348 | 0.0187208 | 6.9 | 6.9 | 6.8 | 5.6 | 6.8 | 5.5 | 8.3 | 0.001322 | 0.0013100 | 0.001301 | 0.000846 | 0.001295 | 0.000825 | 0.001833 | 7.1 | 7.1 | 7.1 | 5.3 | 7.1 | 5.3 | 9.0 | 0.001123 | 0.0011120 | 0.001069 | 0.000660 | 0.001096 | 0.000639 | 0.001626 | 93 | 378 | 2 | 7 |
| 224 | 79987 | -65 | -62 | -61 | -18 | -126 | -64 | -123 | -3 | 0.0256463 | 0.0508806 | 0.0841487 | 0.0169735 | 8.3 | 8.3 | 8.3 | 6.7 | 8.2 | 6.7 | 10.0 | 0.000680 | 0.0006780 | 0.000653 | 0.000115 | 0.000660 | 0.000114 | 0.001257 | 9.0 | 8.9 | 8.8 | 7.0 | 8.9 | 6.9 | 11.1 | 0.000660 | 0.0006570 | 0.000648 | 0.000108 | 0.000640 | 0.000103 | 0.001222 | 130 | 347 | 2 | 6 |
| 225 | 80010 | -287 | -332 | -350 | -517 | -563 | -362 | -574 | -55 | 0.0483057 | 0.1316047 | 0.2211350 | 0.0626915 | 6.1 | 6.0 | 5.7 | 3.9 | 5.9 | 3.7 | 8.6 | 0.000833 | 0.0008310 | 0.000825 | 0.000241 | 0.000812 | 0.000235 | 0.001427 | 6.5 | 6.4 | 6.4 | 3.8 | 6.3 | 3.7 | 9.5 | 0.000660 | 0.0006560 | 0.000653 | 0.000112 | 0.000639 | 0.000104 | 0.001220 | 574 | 784 | 2 | 4 |
| 226 | 80043 | -248 | -217 | -205 | -131 | -473 | -216 | -452 | -1 | 0.0400659 | 0.1169276 | 0.1741683 | 0.0457457 | 7.6 | 7.5 | 7.4 | 5.5 | 7.4 | 5.4 | 10.1 | 0.000950 | 0.0009420 | 0.000936 | 0.000380 | 0.000924 | 0.000372 | 0.001556 | 8.2 | 8.1 | 7.8 | 5.7 | 8.0 | 5.5 | 11.0 | 0.000660 | 0.0006570 | 0.000642 | 0.000108 | 0.000640 | 0.000103 | 0.001222 | 495 | 381 | 2 | 3 |
| 227 | 80157 | -72 | -73 | -74 | -113 | -140 | -78 | -139 | -6 | 0.0235864 | 0.0410959 | 0.0528376 | 0.0125365 | 6.7 | 6.7 | 6.6 | 5.0 | 6.7 | 5.0 | 8.5 | 0.001125 | 0.0011200 | 0.001123 | 0.000533 | 0.001101 | 0.000516 | 0.001741 | 7.2 | 7.2 | 7.1 | 5.1 | 7.1 | 5.0 | 9.4 | 0.000660 | 0.0006560 | 0.000641 | 0.000110 | 0.000640 | 0.000105 | 0.001225 | 143 | 377 | 2 | 8 |
| 228 | 80221 | -249 | -258 | -260 | -272 | -485 | -271 | -493 | -33 | 0.0277063 | 0.0606654 | 0.0919765 | 0.0164897 | 6.9 | 6.8 | 6.5 | 4.7 | 6.7 | 4.6 | 9.4 | 0.001019 | 0.0010120 | 0.000987 | 0.000438 | 0.000993 | 0.000425 | 0.001623 | 7.4 | 7.3 | 7.0 | 4.7 | 7.2 | 4.6 | 10.4 | 0.000659 | 0.0006560 | 0.000639 | 0.000107 | 0.000639 | 0.000102 | 0.001225 | 498 | 348 | 2 | 8 |
| 229 | 80337 | -312 | -324 | -329 | -367 | -607 | -343 | -617 | -37 | 0.0194665 | 0.0459883 | 0.0684932 | 0.0126564 | 6.4 | 6.3 | 6.1 | 4.6 | 6.2 | 4.5 | 8.5 | 0.000577 | 0.0005680 | 0.000543 | 0.000114 | 0.000552 | 0.000095 | 0.001070 | 6.8 | 6.7 | 6.4 | 4.7 | 6.6 | 4.6 | 9.2 | 0.000663 | 0.0006590 | 0.000623 | 0.000145 | 0.000643 | 0.000136 | 0.001192 | 623 | 413 | 2 | 6 |
| 230 | 80450 | -254 | -339 | -370 | -496 | -503 | -379 | -508 | -80 | 0.1121640 | 0.2930528 | 0.4520548 | 0.2601709 | 4.6 | 4.5 | 4.3 | 2.3 | 4.4 | 2.1 | 7.3 | 0.001133 | 0.0011300 | 0.001135 | 0.000485 | 0.001109 | 0.000476 | 0.001794 | 4.8 | 4.7 | 4.5 | 2.0 | 4.6 | 1.9 | 8.0 | 0.000659 | 0.0006550 | 0.000672 | 0.000109 | 0.000638 | 0.000106 | 0.001220 | 508 | 370 | 2 | 6 |
| 231 | 83948 | -248 | -193 | -170 | -18 | -468 | -181 | -442 | 0 | 0.0606654 | 0.1780822 | 0.3111546 | 0.1128510 | 8.7 | 8.6 | 8.5 | 6.8 | 8.6 | 6.7 | 10.8 | 0.000585 | 0.0005800 | 0.000592 | 0.000056 | 0.000563 | 0.000050 | 0.001134 | 9.4 | 9.3 | 9.1 | 7.1 | 9.2 | 7.0 | 11.9 | 0.000660 | 0.0006570 | 0.000642 | 0.000108 | 0.000640 | 0.000105 | 0.001227 | 495 | 223 | 2 | 7 |
| 232 | 84001 | -66 | -66 | -66 | -75 | -129 | -69 | -128 | -4 | 0.0235864 | 0.0362035 | 0.0410959 | 0.0120087 | 7.3 | 7.3 | 7.2 | 5.6 | 7.2 | 5.5 | 9.1 | 0.001201 | 0.0011940 | 0.001188 | 0.000603 | 0.001174 | 0.000589 | 0.001835 | 7.8 | 7.8 | 7.8 | 5.7 | 7.8 | 5.7 | 10.1 | 0.000660 | 0.0006570 | 0.000663 | 0.000112 | 0.000640 | 0.000104 | 0.001222 | 133 | 1027 | 4 | 7 |
| 233 | 84596 | -288 | -270 | -263 | -198 | -556 | -275 | -538 | -3 | 0.0235864 | 0.0704501 | 0.1076321 | 0.0192025 | 7.4 | 7.3 | 7.0 | 5.3 | 7.2 | 5.1 | 9.8 | 0.000742 | 0.0007370 | 0.000768 | 0.000201 | 0.000719 | 0.000185 | 0.001305 | 7.9 | 7.8 | 7.4 | 5.4 | 7.7 | 5.2 | 10.8 | 0.000661 | 0.0006570 | 0.000647 | 0.000110 | 0.000640 | 0.000108 | 0.001224 | 576 | 683 | 3 | 4 |
| 234 | 84976 | -252 | -195 | -173 | -18 | -473 | -183 | -445 | 0 | 0.0689051 | 0.1878669 | 0.3111546 | 0.1188578 | 8.2 | 8.2 | 8.0 | 6.7 | 8.1 | 6.6 | 10.1 | 0.000497 | 0.0004950 | 0.000510 | -0.000011 | 0.000479 | -0.000013 | 0.001018 | 9.0 | 8.9 | 8.7 | 7.1 | 8.8 | 7.0 | 11.1 | 0.000611 | 0.0006080 | 0.000600 | 0.000081 | 0.000592 | 0.000075 | 0.001158 | 503 | 2571 | 7 | 3 |
| 235 | 84D5D | -70 | -69 | -68 | -43 | -137 | -72 | -135 | -4 | 0.0235864 | 0.0313112 | 0.0489237 | 0.0118033 | 7.7 | 7.7 | 7.7 | 6.2 | 7.6 | 6.1 | 9.3 | 0.000731 | 0.0007310 | 0.000734 | 0.000104 | 0.000711 | 0.000105 | 0.001363 | 8.2 | 8.2 | 8.2 | 6.3 | 8.2 | 6.3 | 10.3 | 0.000660 | 0.0006560 | 0.000619 | 0.000111 | 0.000639 | 0.000107 | 0.001222 | 141 | 1134 | 5 | 7 |
| 236 | 85498 | -272 | -198 | -177 | -78 | -492 | -187 | -452 | 0 | 0.1245236 | 0.2685910 | 0.3542074 | 0.1943774 | 7.6 | 7.4 | 7.1 | 5.6 | 7.4 | 5.5 | 9.9 | 0.001390 | 0.0013760 | 0.001350 | 0.000925 | 0.001360 | 0.000902 | 0.001903 | 8.1 | 7.9 | 7.6 | 6.0 | 7.9 | 5.8 | 10.7 | 0.000862 | 0.0008550 | 0.000835 | 0.000494 | 0.000843 | 0.000484 | 0.001255 | 544 | 730 | 4 | 5 |
| 237 | 85589 | -72 | -76 | -78 | -125 | -140 | -81 | -140 | -8 | 0.0256463 | 0.0508806 | 0.0958904 | 0.0182374 | 5.0 | 5.0 | 5.0 | 3.8 | 5.0 | 3.8 | 6.2 | 0.000583 | 0.0005790 | 0.000583 | 0.000061 | 0.000562 | 0.000049 | 0.001123 | 5.1 | 5.1 | 5.1 | 3.7 | 5.1 | 3.7 | 6.6 | 0.000780 | 0.0007710 | 0.000746 | 0.000282 | 0.000755 | 0.000268 | 0.001309 | 143 | 728 | 2 | 6 |
| 238 | 85707 | -194 | -238 | -256 | -373 | -381 | -264 | -387 | -45 | 0.0689051 | 0.1878669 | 0.3189824 | 0.1230069 | 4.9 | 4.8 | 4.7 | 3.2 | 4.8 | 3.1 | 6.8 | 0.001190 | 0.0011780 | 0.001154 | 0.000736 | 0.001163 | 0.000713 | 0.001688 | 5.1 | 5.1 | 4.9 | 3.2 | 5.0 | 3.1 | 7.3 | 0.000844 | 0.0008340 | 0.000806 | 0.000415 | 0.000819 | 0.000398 | 0.001304 | 387 | 502 | 2 | 4 |
| 239 | 85730 | -228 | -186 | -168 | -32 | -433 | -178 | -410 | 0 | 0.0503656 | 0.1487280 | 0.2602740 | 0.0808739 | 8.5 | 8.5 | 8.4 | 7.1 | 8.4 | 7.0 | 10.2 | 0.000477 | 0.0004740 | 0.000459 | 0.000219 | 0.000465 | 0.000214 | 0.000745 | 9.3 | 9.2 | 9.1 | 7.6 | 9.1 | 7.5 | 11.2 | 0.000431 | 0.0004290 | 0.000417 | 0.000067 | 0.000417 | 0.000063 | 0.000800 | 455 | 1093 | 3 | 9 |
| 240 | 86034 | -178 | -191 | -196 | -269 | -349 | -204 | -354 | -24 | 0.0277063 | 0.0680039 | 0.1037182 | 0.0196754 | 6.6 | 6.6 | 6.5 | 5.0 | 6.5 | 4.9 | 8.5 | 0.001007 | 0.0010010 | 0.001004 | 0.000547 | 0.000986 | 0.000538 | 0.001490 | 7.1 | 7.0 | 7.0 | 5.2 | 7.0 | 5.1 | 9.1 | 0.000651 | 0.0006490 | 0.000651 | 0.000118 | 0.000632 | 0.000113 | 0.001201 | 357 | 301 | 2 | 2 |
| 241 | 86076 | -238 | -211 | -198 | -40 | -457 | -209 | -439 | -1 | 0.0338861 | 0.0949119 | 0.1741683 | 0.0378136 | 7.7 | 7.7 | 7.6 | 6.2 | 7.6 | 6.1 | 9.5 | 0.000377 | 0.0003710 | 0.000345 | -0.000015 | 0.000358 | -0.000025 | 0.000791 | 8.3 | 8.3 | 8.2 | 6.5 | 8.2 | 6.4 | 10.4 | 0.000639 | 0.0006360 | 0.000647 | 0.000114 | 0.000620 | 0.000111 | 0.001178 | 477 | 1329 | 5 | 11 |
| 242 | 86335 | -252 | -287 | -303 | -481 | -496 | -315 | -504 | -40 | 0.0318261 | 0.1046967 | 0.1976517 | 0.0479759 | 5.5 | 5.4 | 5.2 | 3.8 | 5.4 | 3.7 | 7.4 | 0.000752 | 0.0007460 | 0.000742 | 0.000206 | 0.000729 | 0.000194 | 0.001318 | 5.7 | 5.6 | 5.4 | 3.6 | 5.5 | 3.5 | 7.9 | 0.000746 | 0.0007400 | 0.000733 | 0.000217 | 0.000724 | 0.000206 | 0.001301 | 505 | 730 | 4 | 4 |
| 243 | 86426 | -320 | -122 | -93 | -13 | -402 | -99 | -340 | 0 | 0.4396951 | 0.6110568 | 0.7064579 | 0.9692861 | 9.9 | 9.8 | 9.7 | 8.2 | 9.7 | 8.1 | 11.8 | 0.000889 | 0.0008800 | 0.000862 | 0.000567 | 0.000869 | 0.000548 | 0.001240 | 10.8 | 10.7 | 10.6 | 8.9 | 10.6 | 8.8 | 12.9 | 0.000626 | 0.0006230 | 0.000616 | 0.000090 | 0.000606 | 0.000082 | 0.001174 | 639 | 2179 | 6 | 4 |
| 244 | 86875 | -231 | -307 | -336 | -449 | -457 | -345 | -462 | -65 | 0.0956844 | 0.2734834 | 0.4520548 | 0.2461540 | 3.1 | 3.0 | 2.7 | 1.0 | 2.9 | 0.8 | 5.7 | 0.001134 | 0.0011280 | 0.001110 | 0.000435 | 0.001105 | 0.000428 | 0.001857 | 3.2 | 3.1 | 2.8 | 0.5 | 3.0 | 0.4 | 6.2 | 0.000660 | 0.0006570 | 0.000669 | 0.000108 | 0.000639 | 0.000099 | 0.001215 | 462 | 712 | 3 | 6 |
| 245 | 86885 | -138 | -87 | -70 | -11 | -248 | -75 | -227 | 0 | 0.1327634 | 0.3224070 | 0.4794521 | 0.3100269 | 10.3 | 10.2 | 10.1 | 8.9 | 10.2 | 8.9 | 11.7 | 0.000735 | 0.0007290 | 0.000719 | 0.000469 | 0.000720 | 0.000458 | 0.001024 | 11.1 | 11.0 | 10.9 | 9.6 | 11.0 | 9.5 | 12.7 | 0.000584 | 0.0005800 | 0.000576 | 0.000341 | 0.000572 | 0.000332 | 0.000840 | 275 | 2200 | 7 | 8 |
| 246 | 86918 | -254 | -279 | -291 | -469 | -499 | -302 | -507 | -35 | 0.0235864 | 0.0777886 | 0.1428571 | 0.0278284 | 6.0 | 5.9 | 5.7 | 4.4 | 5.8 | 4.2 | 7.8 | 0.000527 | 0.0005250 | 0.000531 | 0.000113 | 0.000512 | 0.000108 | 0.000950 | 6.2 | 6.2 | 6.0 | 4.3 | 6.1 | 4.2 | 8.4 | 0.000712 | 0.0007080 | 0.000683 | 0.000188 | 0.000691 | 0.000177 | 0.001255 | 509 | 1549 | 3 | 3 |
| 247 | 87453 | -72 | -78 | -82 | -132 | -140 | -85 | -142 | -10 | 0.0277063 | 0.0728963 | 0.1428571 | 0.0303070 | 4.3 | 4.3 | 4.3 | 3.0 | 4.3 | 3.0 | 5.7 | 0.001298 | 0.0012850 | 0.001261 | 0.000804 | 0.001268 | 0.000773 | 0.001833 | 4.4 | 4.4 | 4.4 | 3.0 | 4.4 | 2.9 | 5.9 | 0.000900 | 0.0008880 | 0.000880 | 0.000456 | 0.000873 | 0.000435 | 0.001388 | 143 | 1471 | 5 | 6 |
| 248 | 87479 | -189 | -180 | -175 | -18 | -367 | -185 | -357 | -4 | 0.0174065 | 0.0410959 | 0.0763209 | 0.0117775 | 7.7 | 7.6 | 7.6 | 6.0 | 7.6 | 5.9 | 9.6 | 0.000490 | 0.0004870 | 0.000454 | 0.000061 | 0.000473 | 0.000051 | 0.000923 | 8.3 | 8.2 | 8.1 | 6.1 | 8.2 | 6.0 | 10.6 | 0.000660 | 0.0006570 | 0.000638 | 0.000109 | 0.000640 | 0.000105 | 0.001221 | 378 | 351 | 2 | 4 |
| 249 | 87517 | -238 | -221 | -213 | -12 | -463 | -226 | -451 | -2 | 0.0050469 | 0.0239726 | 0.0958904 | 0.0202026 | 6.6 | 6.6 | 6.4 | 5.2 | 6.5 | 5.1 | 8.3 | 0.000395 | 0.0003910 | 0.000380 | 0.000205 | 0.000385 | 0.000198 | 0.000597 | 6.5 | 6.5 | 6.3 | 4.7 | 6.4 | 4.6 | 8.6 | 0.000387 | 0.0003870 | 0.000377 | 0.000012 | 0.000375 | 0.000009 | 0.000760 | 475 | 632 | 2 | 3 |
| 250 | 88415 | -358 | -439 | -468 | -695 | -703 | -482 | -715 | -87 | 0.0750850 | 0.2049902 | 0.3111546 | 0.1275851 | 5.5 | 5.4 | 5.3 | 3.4 | 5.3 | 3.2 | 7.9 | 0.001022 | 0.0010100 | 0.000977 | 0.000548 | 0.000993 | 0.000529 | 0.001545 | 5.8 | 5.7 | 5.6 | 3.3 | 5.6 | 3.2 | 8.5 | 0.000740 | 0.0007350 | 0.000718 | 0.000223 | 0.000718 | 0.000214 | 0.001278 | 715 | 1082 | 3 | 3 |
| 251 | 88507 | -111 | -109 | -108 | -20 | -216 | -114 | -213 | -6 | 0.0194665 | 0.0313112 | 0.0371820 | 0.0085719 | 7.2 | 7.1 | 7.1 | 5.8 | 7.1 | 5.7 | 8.6 | 0.000490 | 0.0004850 | 0.000476 | 0.000255 | 0.000478 | 0.000249 | 0.000743 | 7.7 | 7.7 | 7.8 | 6.1 | 7.7 | 6.1 | 9.4 | 0.000647 | 0.0006440 | 0.000626 | 0.000105 | 0.000628 | 0.000102 | 0.001201 | 222 | 1334 | 3 | 6 |
| 252 | 88652 | -179 | -213 | -231 | -345 | -353 | -239 | -358 | -31 | 0.0380060 | 0.1316047 | 0.2837573 | 0.0875657 | 4.4 | 4.4 | 4.3 | 3.0 | 4.3 | 3.0 | 5.9 | 0.000540 | 0.0005390 | 0.000574 | -0.000087 | 0.000519 | -0.000088 | 0.001176 | 4.2 | 4.2 | 4.1 | 2.3 | 4.1 | 2.3 | 6.1 | 0.000803 | 0.0007920 | 0.000775 | 0.000248 | 0.000775 | 0.000229 | 0.001394 | 358 | 1952 | 4 | 9 |
| 253 | 89282 | -284 | -361 | -395 | -556 | -561 | -407 | -568 | -61 | 0.0606654 | 0.2074364 | 0.3894325 | 0.1702587 | 4.1 | 4.0 | 3.9 | 2.5 | 4.0 | 2.3 | 6.0 | 0.000563 | 0.0005570 | 0.000562 | -0.000015 | 0.000539 | -0.000023 | 0.001169 | 4.0 | 3.9 | 3.7 | 1.8 | 3.8 | 1.7 | 6.3 | 0.000827 | 0.0008180 | 0.000835 | 0.000277 | 0.000799 | 0.000261 | 0.001415 | 568 | 2197 | 9 | 6 |
| 254 | 90599 | -252 | -313 | -337 | -485 | -498 | -347 | -505 | -60 | 0.0730250 | 0.2025440 | 0.3307241 | 0.1341694 | 4.9 | 4.8 | 4.8 | 3.0 | 4.7 | 2.9 | 7.0 | 0.001096 | 0.0010860 | 0.001058 | 0.000586 | 0.001069 | 0.000571 | 0.001649 | 5.0 | 5.0 | 4.9 | 2.9 | 4.9 | 2.8 | 7.4 | 0.000712 | 0.0007070 | 0.000694 | 0.000180 | 0.000691 | 0.000169 | 0.001259 | 505 | 1434 | 7 | 9 |
| 255 | 90697 | -127 | -124 | -122 | -92 | -247 | -128 | -243 | -5 | 0.0174065 | 0.0337573 | 0.0489237 | 0.0088832 | 7.3 | 7.3 | 7.3 | 5.8 | 7.2 | 5.8 | 8.9 | 0.000673 | 0.0006620 | 0.000633 | 0.000348 | 0.000651 | 0.000326 | 0.001034 | 7.9 | 7.9 | 7.8 | 6.2 | 7.8 | 6.1 | 9.7 | 0.000650 | 0.0006470 | 0.000651 | 0.000106 | 0.000631 | 0.000103 | 0.001204 | 254 | 406 | 3 | 8 |
| 256 | 91036 | -238 | -105 | -80 | -11 | -345 | -85 | -294 | 0 | 0.3490576 | 0.5425636 | 0.6594912 | 0.7647237 | 9.1 | 9.0 | 8.8 | 7.5 | 9.0 | 7.3 | 11.0 | 0.001115 | 0.0011030 | 0.001085 | 0.000770 | 0.001091 | 0.000746 | 0.001503 | 9.7 | 9.6 | 9.5 | 7.9 | 9.6 | 7.8 | 11.9 | 0.000690 | 0.0006870 | 0.000683 | 0.000152 | 0.000670 | 0.000149 | 0.001246 | 476 | 1939 | 3 | 5 |
| 257 | 91426 | -189 | -178 | -174 | -14 | -367 | -183 | -357 | -4 | 0.0153466 | 0.0410959 | 0.0841487 | 0.0129389 | 7.3 | 7.2 | 7.2 | 6.1 | 7.2 | 6.0 | 8.6 | 0.000466 | 0.0004610 | 0.000444 | 0.000287 | 0.000455 | 0.000279 | 0.000660 | 8.1 | 8.0 | 7.9 | 6.7 | 8.0 | 6.6 | 9.6 | 0.000477 | 0.0004720 | 0.000455 | 0.000121 | 0.000461 | 0.000111 | 0.000851 | 378 | 379 | 2 | 6 |
| 258 | 92210 | -196 | -73 | -55 | -10 | -244 | -59 | -206 | 0 | 0.4458750 | 0.6159491 | 0.7103718 | 0.9952802 | 10.2 | 10.1 | 9.9 | 8.7 | 10.0 | 8.6 | 11.8 | 0.001117 | 0.0011120 | 0.001103 | 0.000837 | 0.001103 | 0.000826 | 0.001412 | 10.9 | 10.8 | 10.7 | 9.3 | 10.8 | 9.2 | 12.9 | 0.000927 | 0.0009230 | 0.000916 | 0.000597 | 0.000912 | 0.000590 | 0.001267 | 391 | 837 | 3 | 7 |
| 259 | 92299 | -174 | -133 | -116 | -19 | -327 | -123 | -307 | 0 | 0.0689051 | 0.1976517 | 0.3268102 | 0.1295858 | 8.9 | 8.8 | 8.7 | 7.6 | 8.8 | 7.5 | 10.5 | 0.000632 | 0.0006270 | 0.000629 | 0.000166 | 0.000612 | 0.000154 | 0.001117 | 9.6 | 9.5 | 9.3 | 8.1 | 9.5 | 8.0 | 11.4 | 0.000642 | 0.0006390 | 0.000623 | 0.000134 | 0.000624 | 0.000128 | 0.001157 | 347 | 1463 | 5 | 4 |
| 260 | 93678 | -229 | -237 | -240 | -309 | -447 | -251 | -453 | -25 | 0.0174065 | 0.0435421 | 0.0645793 | 0.0106538 | 6.8 | 6.8 | 6.7 | 4.9 | 6.7 | 4.8 | 9.1 | 0.000764 | 0.0007450 | 0.000712 | 0.000341 | 0.000730 | 0.000311 | 0.001256 | 7.3 | 7.3 | 7.1 | 4.9 | 7.2 | 4.8 | 10.0 | 0.000660 | 0.0006570 | 0.000679 | 0.000109 | 0.000640 | 0.000103 | 0.001224 | 458 | 918 | 2 | 8 |
| 261 | 93706 | -174 | -181 | -184 | -238 | -340 | -193 | -344 | -18 | 0.0194665 | 0.0459883 | 0.0684932 | 0.0113046 | 6.8 | 6.8 | 6.6 | 4.9 | 6.7 | 4.8 | 9.0 | 0.000925 | 0.0009210 | 0.000923 | 0.000345 | 0.000902 | 0.000335 | 0.001523 | 7.3 | 7.3 | 7.0 | 4.9 | 7.2 | 4.8 | 9.9 | 0.000661 | 0.0006580 | 0.000625 | 0.000114 | 0.000641 | 0.000108 | 0.001219 | 349 | 708 | 3 | 5 |
| 262 | 95733 | -230 | -191 | -175 | -32 | -437 | -185 | -414 | 0 | 0.0544855 | 0.1511742 | 0.2407045 | 0.0743335 | 7.9 | 7.9 | 7.6 | 5.9 | 7.8 | 5.8 | 10.3 | 0.000944 | 0.0009350 | 0.000924 | 0.000485 | 0.000920 | 0.000464 | 0.001430 | 8.5 | 8.5 | 8.3 | 6.1 | 8.4 | 5.9 | 11.4 | 0.000658 | 0.0006560 | 0.000695 | 0.000108 | 0.000639 | 0.000107 | 0.001222 | 461 | 1191 | 3 | 6 |
| 263 | 96261 | -170 | -185 | -192 | -326 | -333 | -200 | -338 | -22 | 0.0215264 | 0.0704501 | 0.1350294 | 0.0241859 | 5.5 | 5.4 | 5.3 | 4.0 | 5.4 | 3.9 | 7.1 | 0.000900 | 0.0008890 | 0.000867 | 0.000502 | 0.000876 | 0.000482 | 0.001334 | 5.7 | 5.6 | 5.6 | 4.1 | 5.6 | 4.0 | 7.5 | 0.000699 | 0.0006930 | 0.000681 | 0.000336 | 0.000681 | 0.000324 | 0.001088 | 340 | 1418 | 3 | 5 |
| 264 | 96353 | -146 | -115 | -103 | -13 | -276 | -109 | -259 | 0 | 0.0689051 | 0.1854207 | 0.2954990 | 0.1131757 | 9.0 | 9.0 | 9.0 | 7.4 | 8.9 | 7.3 | 10.7 | 0.000913 | 0.0009010 | 0.000874 | 0.000513 | 0.000887 | 0.000492 | 0.001352 | 9.8 | 9.7 | 9.6 | 8.0 | 9.7 | 7.9 | 11.7 | 0.000639 | 0.0006360 | 0.000638 | 0.000099 | 0.000620 | 0.000097 | 0.001190 | 293 | 816 | 3 | 4 |
| 265 | 96382 | -273 | -279 | -282 | -358 | -531 | -294 | -538 | -26 | 0.0132866 | 0.0337573 | 0.0606654 | 0.0099502 | 6.8 | 6.7 | 6.5 | 5.0 | 6.6 | 5.0 | 8.7 | 0.000538 | 0.0005330 | 0.000530 | 0.000150 | 0.000521 | 0.000142 | 0.000945 | 7.2 | 7.1 | 6.9 | 5.2 | 7.0 | 5.1 | 9.4 | 0.000697 | 0.0006920 | 0.000693 | 0.000177 | 0.000677 | 0.000171 | 0.001238 | 546 | 1818 | 7 | 5 |
| 266 | 96419 | -162 | -155 | -153 | -22 | -314 | -160 | -307 | -4 | 0.0153466 | 0.0337573 | 0.0606654 | 0.0098524 | 7.6 | 7.6 | 7.4 | 5.9 | 7.5 | 5.9 | 9.5 | 0.000564 | 0.0005560 | 0.000552 | 0.000205 | 0.000544 | 0.000188 | 0.000950 | 8.2 | 8.2 | 8.0 | 6.1 | 8.1 | 6.0 | 10.5 | 0.000659 | 0.0006560 | 0.000645 | 0.000108 | 0.000639 | 0.000102 | 0.001221 | 323 | 349 | 2 | 2 |
| 267 | 96B79 | -210 | -207 | -206 | -236 | -408 | -216 | -398 | -7 | 0.0215264 | 0.0435421 | 0.0724070 | 0.0112173 | 7.5 | 7.5 | 7.4 | 5.8 | 7.4 | 5.7 | 9.5 | 0.000896 | 0.0008800 | 0.000841 | 0.000472 | 0.000865 | 0.000445 | 0.001373 | 8.3 | 8.2 | 8.1 | 6.4 | 8.1 | 6.3 | 10.5 | 0.000707 | 0.0006990 | 0.000678 | 0.000253 | 0.000684 | 0.000239 | 0.001193 | 421 | 1242 | 2 | 4 |
| 268 | 98152 | -230 | -250 | -258 | -438 | -451 | -269 | -459 | -30 | 0.0215264 | 0.0680039 | 0.1193738 | 0.0219019 | 6.3 | 6.2 | 6.0 | 4.4 | 6.2 | 4.3 | 8.5 | 0.000669 | 0.0006660 | 0.000660 | 0.000195 | 0.000650 | 0.000183 | 0.001159 | 6.7 | 6.7 | 6.6 | 4.3 | 6.6 | 4.3 | 9.4 | 0.000662 | 0.0006580 | 0.000638 | 0.000114 | 0.000641 | 0.000108 | 0.001226 | 461 | 742 | 2 | 7 |
| 269 | 98195 | -108 | -112 | -114 | -200 | -210 | -120 | -211 | -11 | 0.0215264 | 0.0459883 | 0.0763209 | 0.0122514 | 5.9 | 5.8 | 5.7 | 4.5 | 5.8 | 4.4 | 7.3 | 0.001127 | 0.0011160 | 0.001092 | 0.000744 | 0.001103 | 0.000722 | 0.001547 | 6.1 | 6.1 | 6.0 | 4.5 | 6.0 | 4.5 | 7.8 | 0.000769 | 0.0007620 | 0.000749 | 0.000269 | 0.000747 | 0.000261 | 0.001299 | 215 | 349 | 2 | 6 |
| 270 | 98521 | -241 | -323 | -353 | -470 | -477 | -362 | -482 | -70 | 0.0998043 | 0.2930528 | 0.4598826 | 0.2649646 | 3.7 | 3.6 | 3.5 | 1.5 | 3.6 | 1.4 | 6.3 | 0.001102 | 0.0010960 | 0.001032 | 0.000430 | 0.001074 | 0.000428 | 0.001802 | 3.9 | 3.8 | 3.7 | 1.2 | 3.7 | 1.1 | 6.9 | 0.000659 | 0.0006560 | 0.000652 | 0.000110 | 0.000639 | 0.000108 | 0.001227 | 482 | 345 | 2 | 3 |
| 271 | 99121 | -172 | -191 | -200 | -333 | -338 | -208 | -345 | -26 | 0.0277063 | 0.0826810 | 0.1585127 | 0.0322792 | 5.5 | 5.4 | 5.5 | 3.9 | 5.4 | 3.8 | 7.2 | 0.000767 | 0.0007510 | 0.000712 | 0.000286 | 0.000734 | 0.000259 | 0.001310 | 5.7 | 5.7 | 5.7 | 3.9 | 5.6 | 3.9 | 7.7 | 0.000695 | 0.0006900 | 0.000692 | 0.000157 | 0.000673 | 0.000153 | 0.001254 | 345 | 869 | 3 | 5 |
| 272 | 99842 | -186 | -160 | -148 | -30 | -357 | -157 | -340 | 0 | 0.0400659 | 0.1193738 | 0.2015656 | 0.0531956 | 8.0 | 7.9 | 7.7 | 6.4 | 7.9 | 6.3 | 9.8 | 0.000724 | 0.0007150 | 0.000720 | 0.000300 | 0.000701 | 0.000282 | 0.001185 | 8.5 | 8.5 | 8.2 | 6.7 | 8.4 | 6.6 | 10.6 | 0.000707 | 0.0007010 | 0.000700 | 0.000318 | 0.000688 | 0.000305 | 0.001122 | 373 | 537 | 2 | 8 |
| 273 | A2B2C | -256 | -275 | -282 | -348 | -498 | -293 | -508 | -39 | 0.0338861 | 0.0900196 | 0.1272016 | 0.0291650 | 6.7 | 6.6 | 6.6 | 4.4 | 6.6 | 4.3 | 9.4 | 0.001116 | 0.0011090 | 0.001120 | 0.000528 | 0.001090 | 0.000516 | 0.001738 | 7.2 | 7.1 | 6.8 | 4.4 | 7.0 | 4.3 | 10.3 | 0.000659 | 0.0006560 | 0.000661 | 0.000112 | 0.000639 | 0.000108 | 0.001227 | 511 | 3081 | 4 | 4 |
| 274 | A3039 | -271 | -356 | -387 | -529 | -536 | -396 | -542 | -77 | 0.0977444 | 0.2759295 | 0.4168297 | 0.2280764 | 5.0 | 4.9 | 4.6 | 2.6 | 4.8 | 2.5 | 7.6 | 0.001060 | 0.0010590 | 0.001073 | 0.000425 | 0.001038 | 0.000420 | 0.001706 | 5.2 | 5.1 | 5.0 | 2.4 | 5.0 | 2.3 | 8.3 | 0.000660 | 0.0006570 | 0.000648 | 0.000110 | 0.000640 | 0.000112 | 0.001226 | 542 | 1090 | 2 | 4 |
| 275 | A543D | -230 | -262 | -275 | -434 | -450 | -285 | -459 | -42 | 0.0441858 | 0.1267123 | 0.1976517 | 0.0532674 | 6.3 | 6.2 | 6.0 | 4.1 | 6.1 | 4.0 | 8.8 | 0.001074 | 0.0010650 | 0.001064 | 0.000541 | 0.001048 | 0.000520 | 0.001633 | 6.7 | 6.6 | 6.4 | 4.1 | 6.5 | 3.9 | 9.7 | 0.000660 | 0.0006560 | 0.000643 | 0.000110 | 0.000639 | 0.000104 | 0.001224 | 459 | 752 | 3 | 8 |
| 276 | A6234 | -146 | -114 | -100 | -13 | -276 | -106 | -260 | 0 | 0.0668452 | 0.1829746 | 0.3111546 | 0.1184918 | 9.4 | 9.4 | 9.2 | 7.9 | 9.3 | 7.8 | 11.1 | 0.000692 | 0.0006850 | 0.000659 | 0.000264 | 0.000671 | 0.000251 | 0.001143 | 10.3 | 10.3 | 10.1 | 8.6 | 10.2 | 8.5 | 12.2 | 0.000605 | 0.0006040 | 0.000596 | 0.000056 | 0.000588 | 0.000053 | 0.001155 | 293 | 2221 | 7 | 6 |
| 277 | A712D | -230 | -255 | -265 | -419 | -451 | -276 | -461 | -37 | 0.0338861 | 0.0998043 | 0.1506849 | 0.0350215 | 6.0 | 5.9 | 5.6 | 4.4 | 5.9 | 4.3 | 7.9 | 0.001032 | 0.0010190 | 0.001000 | 0.000556 | 0.001003 | 0.000529 | 0.001546 | 6.3 | 6.3 | 6.1 | 4.5 | 6.2 | 4.4 | 8.4 | 0.000745 | 0.0007400 | 0.000720 | 0.000275 | 0.000725 | 0.000266 | 0.001236 | 461 | 312 | 2 | 6 |
| 278 | B0712 | -234 | -268 | -282 | -430 | -458 | -292 | -467 | -44 | 0.0462458 | 0.1340509 | 0.2133072 | 0.0594917 | 7.0 | 6.9 | 6.8 | 5.1 | 6.9 | 5.0 | 9.0 | 0.000932 | 0.0009170 | 0.000879 | 0.000461 | 0.000901 | 0.000438 | 0.001464 | 7.6 | 7.5 | 7.5 | 5.5 | 7.5 | 5.4 | 9.9 | 0.000574 | 0.0005730 | 0.000590 | 0.000042 | 0.000557 | 0.000039 | 0.001109 | 467 | 1858 | 4 | 7 |
| 279 | B0F3B | -203 | -141 | -121 | -12 | -370 | -128 | -341 | 0 | 0.1162839 | 0.2783757 | 0.4011742 | 0.2198181 | 9.0 | 8.9 | 8.7 | 7.4 | 8.9 | 7.2 | 10.9 | 0.000918 | 0.0009110 | 0.000891 | 0.000527 | 0.000897 | 0.000513 | 0.001339 | 9.8 | 9.7 | 9.6 | 7.9 | 9.7 | 7.8 | 11.9 | 0.000626 | 0.0006230 | 0.000631 | 0.000092 | 0.000607 | 0.000087 | 0.001170 | 406 | 0 | 1 | 5 |
| 280 | B1311 | -146 | -150 | -153 | -244 | -284 | -160 | -286 | -14 | 0.0174065 | 0.0386497 | 0.0567515 | 0.0097511 | 7.1 | 7.1 | 7.0 | 5.8 | 7.1 | 5.7 | 8.5 | 0.000620 | 0.0006130 | 0.000600 | 0.000388 | 0.000605 | 0.000377 | 0.000879 | 7.8 | 7.7 | 7.7 | 6.3 | 7.7 | 6.2 | 9.3 | 0.000355 | 0.0003520 | 0.000346 | 0.000156 | 0.000346 | 0.000148 | 0.000562 | 291 | 1660 | 4 | 4 |
| 281 | B6069 | -74 | -75 | -75 | -131 | -143 | -79 | -143 | -6 | 0.0215264 | 0.0313112 | 0.0489237 | 0.0111567 | 5.9 | 5.9 | 5.8 | 4.6 | 5.8 | 4.6 | 7.2 | 0.000952 | 0.0009470 | 0.000917 | 0.000352 | 0.000928 | 0.000343 | 0.001572 | 6.1 | 6.1 | 6.0 | 4.6 | 6.0 | 4.6 | 7.6 | 0.000782 | 0.0007740 | 0.000755 | 0.000274 | 0.000758 | 0.000257 | 0.001316 | 147 | 661 | 2 | 9 |
| 282 | C0102 | -232 | -190 | -173 | -24 | -438 | -183 | -416 | 0 | 0.0544855 | 0.1536204 | 0.2524462 | 0.0797689 | 8.3 | 8.2 | 8.1 | 6.6 | 8.2 | 6.5 | 10.2 | 0.000623 | 0.0006140 | 0.000600 | 0.000383 | 0.000606 | 0.000367 | 0.000895 | 9.0 | 8.9 | 8.7 | 7.1 | 8.9 | 7.0 | 11.1 | 0.000619 | 0.0006150 | 0.000586 | 0.000090 | 0.000599 | 0.000082 | 0.001158 | 463 | 1463 | 4 | 4 |
| 283 | C0D60 | -76 | -81 | -83 | -138 | -150 | -86 | -150 | -8 | 0.0235864 | 0.0508806 | 0.0919765 | 0.0163044 | 6.6 | 6.6 | 6.6 | 5.2 | 6.6 | 5.1 | 8.1 | 0.000908 | 0.0009050 | 0.000905 | 0.000224 | 0.000883 | 0.000218 | 0.001607 | 7.2 | 7.2 | 7.2 | 5.5 | 7.1 | 5.5 | 8.9 | 0.000639 | 0.0006380 | 0.000633 | 0.000090 | 0.000621 | 0.000081 | 0.001190 | 153 | 1478 | 6 | 5 |
| 284 | C3530 | -145 | -164 | -173 | -276 | -285 | -180 | -289 | -22 | 0.0277063 | 0.0900196 | 0.1859100 | 0.0428823 | 4.1 | 4.0 | 4.0 | 2.6 | 4.0 | 2.6 | 5.6 | 0.001048 | 0.0010340 | 0.001005 | 0.000551 | 0.001017 | 0.000529 | 0.001596 | 3.9 | 3.9 | 3.9 | 2.2 | 3.8 | 2.2 | 5.8 | 0.000935 | 0.0009210 | 0.000895 | 0.000466 | 0.000906 | 0.000444 | 0.001452 | 290 | 671 | 2 | 5 |
| 285 | C667D | -175 | -205 | -218 | -336 | -344 | -225 | -350 | -34 | 0.0483057 | 0.1364971 | 0.2446184 | 0.0734522 | 5.2 | 5.2 | 5.0 | 3.5 | 5.1 | 3.4 | 7.1 | 0.001295 | 0.0012820 | 0.001274 | 0.000812 | 0.001265 | 0.000785 | 0.001829 | 5.5 | 5.4 | 5.3 | 3.6 | 5.3 | 3.5 | 7.5 | 0.000739 | 0.0007310 | 0.000734 | 0.000278 | 0.000716 | 0.000268 | 0.001230 | 350 | 1118 | 6 | 7 |
| 286 | D2B53 | -288 | -109 | -84 | -12 | -354 | -89 | -299 | 0 | 0.4541147 | 0.6135029 | 0.7064579 | 0.9845599 | 9.2 | 9.1 | 8.9 | 7.5 | 9.0 | 7.3 | 11.2 | 0.001193 | 0.0011820 | 0.001169 | 0.000725 | 0.001166 | 0.000700 | 0.001698 | 9.9 | 9.7 | 9.5 | 7.9 | 9.7 | 7.7 | 12.2 | 0.000935 | 0.0009250 | 0.000887 | 0.000533 | 0.000912 | 0.000516 | 0.001375 | 576 | 987 | 2 | 10 |
| 287 | D353A | -327 | -314 | -308 | -82 | -634 | -323 | -620 | -6 | 0.0112267 | 0.0362035 | 0.0606654 | 0.0101233 | 6.7 | 6.6 | 6.5 | 5.2 | 6.6 | 5.1 | 8.5 | 0.000388 | 0.0003800 | 0.000349 | 0.000092 | 0.000370 | 0.000079 | 0.000712 | 7.3 | 7.2 | 6.9 | 5.6 | 7.1 | 5.5 | 9.3 | 0.000363 | 0.0003610 | 0.000365 | -0.000026 | 0.000349 | -0.000033 | 0.000759 | 654 | 1663 | 5 | 9 |
| 288 | D6642 | -352 | -379 | -395 | -640 | -690 | -412 | -704 | -43 | 0.0132866 | 0.0533268 | 0.1193738 | 0.0191186 | 6.1 | 6.0 | 5.7 | 4.5 | 6.0 | 4.4 | 8.1 | 0.000599 | 0.0005900 | 0.000571 | 0.000310 | 0.000580 | 0.000293 | 0.000920 | 6.3 | 6.2 | 6.0 | 4.3 | 6.1 | 4.2 | 8.6 | 0.000644 | 0.0006390 | 0.000623 | 0.000307 | 0.000628 | 0.000298 | 0.001000 | 705 | 747 | 4 | 4 |
| 289 | D7A14 | -268 | -254 | -248 | -191 | -517 | -260 | -500 | -4 | 0.0235864 | 0.0582192 | 0.0919765 | 0.0155395 | 7.1 | 7.0 | 6.8 | 5.3 | 6.9 | 5.2 | 9.1 | 0.000682 | 0.0006730 | 0.000661 | 0.000221 | 0.000659 | 0.000208 | 0.001176 | 7.6 | 7.5 | 7.2 | 5.6 | 7.4 | 5.5 | 9.9 | 0.000632 | 0.0006280 | 0.000605 | 0.000222 | 0.000615 | 0.000210 | 0.001063 | 535 | 1461 | 2 | 3 |
| 290 | E022A | -172 | -156 | -148 | -13 | -333 | -157 | -321 | -1 | 0.0215264 | 0.0631115 | 0.1389432 | 0.0248034 | 7.6 | 7.6 | 7.6 | 6.6 | 7.6 | 6.6 | 8.7 | 0.000112 | 0.0001090 | 0.000091 | -0.000116 | 0.000102 | -0.000121 | 0.000351 | 8.3 | 8.3 | 8.3 | 7.1 | 8.2 | 7.0 | 9.6 | 0.000208 | 0.0002060 | 0.000207 | -0.000135 | 0.000196 | -0.000137 | 0.000558 | 344 | 722 | 3 | 4 |
| 291 | E0472 | -340 | -458 | -494 | -664 | -672 | -505 | -680 | -125 | 0.1410032 | 0.3224070 | 0.4481409 | 0.2847776 | 5.3 | 5.3 | 5.1 | 3.0 | 5.2 | 2.9 | 7.9 | 0.001242 | 0.0012320 | 0.001210 | 0.000734 | 0.001215 | 0.000716 | 0.001785 | 5.6 | 5.6 | 5.4 | 3.1 | 5.5 | 2.9 | 8.5 | 0.000857 | 0.0008470 | 0.000804 | 0.000436 | 0.000833 | 0.000419 | 0.001317 | 680 | 2414 | 6 | 7 |
| 292 | E1154 | -154 | -185 | -199 | -294 | -302 | -206 | -307 | -31 | 0.0524256 | 0.1609589 | 0.2915851 | 0.1013623 | 3.2 | 3.2 | 3.1 | 1.8 | 3.1 | 1.8 | 4.6 | 0.000909 | 0.0008860 | 0.000841 | 0.000434 | 0.000869 | 0.000402 | 0.001466 | 3.3 | 3.3 | 3.2 | 2.0 | 3.2 | 1.9 | 4.7 | 0.000139 | 0.0001300 | 0.000112 | -0.000130 | 0.000121 | -0.000146 | 0.000441 | 307 | 629 | 3 | 6 |
| 293 | E1B15 | -309 | -93 | -62 | -10 | -356 | -66 | -291 | 0 | 0.5035534 | 0.6942270 | 0.7964775 | 1.3077307 | 8.7 | 8.7 | 8.4 | 7.2 | 8.6 | 7.0 | 10.6 | 0.001128 | 0.0011140 | 0.001080 | 0.000670 | 0.001098 | 0.000644 | 0.001631 | 9.1 | 9.0 | 8.9 | 7.3 | 9.0 | 7.1 | 11.3 | 0.000719 | 0.0007090 | 0.000694 | 0.000350 | 0.000697 | 0.000327 | 0.001117 | 618 | 1851 | 4 | 2 |
| 294 | E1D7D | -126 | -88 | -74 | -12 | -231 | -79 | -214 | 0 | 0.1080441 | 0.2661448 | 0.4011742 | 0.2155194 | 9.4 | 9.3 | 9.2 | 7.9 | 9.3 | 7.8 | 11.1 | 0.001024 | 0.0010150 | 0.001011 | 0.000731 | 0.001006 | 0.000715 | 0.001347 | 10.1 | 10.0 | 10.0 | 8.4 | 10.0 | 8.3 | 12.1 | 0.000959 | 0.0009500 | 0.000931 | 0.000677 | 0.000941 | 0.000663 | 0.001274 | 252 | 1014 | 3 | 9 |
| 295 | E2204 | -340 | -409 | -438 | -640 | -668 | -452 | -679 | -73 | 0.0606654 | 0.1707436 | 0.2915851 | 0.1006191 | 5.7 | 5.6 | 5.5 | 3.6 | 5.5 | 3.5 | 8.1 | 0.000907 | 0.0008990 | 0.000891 | 0.000452 | 0.000884 | 0.000435 | 0.001389 | 5.8 | 5.7 | 5.5 | 3.4 | 5.6 | 3.2 | 8.6 | 0.000971 | 0.0009640 | 0.000953 | 0.000513 | 0.000948 | 0.000501 | 0.001452 | 679 | 859 | 4 | 5 |
| 296 | E6D47 | -350 | -457 | -501 | -686 | -692 | -514 | -700 | -85 | 0.0750850 | 0.2490215 | 0.4285714 | 0.2141896 | 4.1 | 4.0 | 3.9 | 2.2 | 4.0 | 2.1 | 6.3 | 0.000681 | 0.0006630 | 0.000611 | 0.000158 | 0.000644 | 0.000127 | 0.001267 | 4.0 | 3.9 | 3.8 | 1.6 | 3.9 | 1.4 | 6.7 | 0.000841 | 0.0008320 | 0.000794 | 0.000290 | 0.000814 | 0.000275 | 0.001428 | 700 | 0 | 1 | 5 |
| 297 | E7E64 | -365 | -383 | -388 | -489 | -710 | -404 | -726 | -49 | 0.0235864 | 0.0631115 | 0.0998043 | 0.0191771 | 6.7 | 6.6 | 6.5 | 4.5 | 6.5 | 4.3 | 9.3 | 0.000704 | 0.0006980 | 0.000702 | 0.000226 | 0.000683 | 0.000212 | 0.001207 | 7.2 | 7.1 | 6.8 | 4.5 | 7.0 | 4.3 | 10.2 | 0.000658 | 0.0006550 | 0.000637 | 0.000111 | 0.000638 | 0.000101 | 0.001216 | 730 | 1485 | 3 | 8 |
| 298 | F1071 | -213 | -184 | -171 | -12 | -407 | -181 | -389 | 0 | 0.0380060 | 0.1095890 | 0.1976517 | 0.0484110 | 8.3 | 8.2 | 8.1 | 6.7 | 8.2 | 6.6 | 10.0 | 0.000624 | 0.0006160 | 0.000601 | 0.000417 | 0.000609 | 0.000404 | 0.000854 | 9.0 | 8.9 | 8.9 | 7.2 | 8.9 | 7.1 | 11.0 | 0.000503 | 0.0004980 | 0.000499 | 0.000283 | 0.000491 | 0.000274 | 0.000738 | 426 | 711 | 5 | 6 |
| 299 | F1555 | -223 | -194 | -181 | -36 | -427 | -191 | -408 | -1 | 0.0400659 | 0.1144814 | 0.1898239 | 0.0470253 | 7.7 | 7.6 | 7.5 | 6.2 | 7.6 | 6.1 | 9.5 | 0.000768 | 0.0007600 | 0.000742 | 0.000332 | 0.000746 | 0.000320 | 0.001234 | 8.3 | 8.2 | 8.0 | 6.6 | 8.2 | 6.5 | 10.4 | 0.000589 | 0.0005850 | 0.000577 | 0.000197 | 0.000573 | 0.000191 | 0.000994 | 446 | 321 | 2 | 7 |
| 300 | F5F25 | -234 | -310 | -336 | -455 | -464 | -345 | -469 | -73 | 0.1121640 | 0.2832681 | 0.4324853 | 0.2399924 | 4.8 | 4.8 | 4.7 | 3.0 | 4.7 | 2.9 | 6.8 | 0.001162 | 0.0011480 | 0.001118 | 0.000656 | 0.001131 | 0.000628 | 0.001707 | 5.1 | 5.1 | 4.9 | 3.1 | 5.0 | 3.0 | 7.4 | 0.000676 | 0.0006710 | 0.000682 | 0.000213 | 0.000657 | 0.000202 | 0.001157 | 469 | 1407 | 5 | 7 |
| 301 | 03596 | -112 | -82 | -71 | -12 | -209 | -76 | -195 | 0 | 0.0895046 | 0.2294521 | 0.3620352 | 0.1703523 | 9.4 | 9.4 | 9.3 | 8.0 | 9.3 | 7.9 | 11.0 | 0.001098 | 0.0010870 | 0.001058 | 0.000711 | 0.001074 | 0.000691 | 0.001519 | 10.2 | 10.2 | 10.0 | 8.6 | 10.1 | 8.5 | 12.1 | 0.000777 | 0.0007700 | 0.000755 | 0.000444 | 0.000759 | 0.000433 | 0.001138 | 225 | 2187 | 5 | 0 |
| 302 | 13726 | -179 | -184 | -186 | -278 | -349 | -195 | -352 | -18 | 0.0153466 | 0.0337573 | 0.0606654 | 0.0088941 | 6.9 | 6.8 | 6.7 | 5.3 | 6.8 | 5.2 | 8.6 | 0.000721 | 0.0007180 | 0.000717 | 0.000294 | 0.000704 | 0.000286 | 0.001164 | 7.5 | 7.4 | 7.2 | 5.7 | 7.4 | 5.6 | 9.4 | 0.000609 | 0.0006080 | 0.000604 | 0.000069 | 0.000591 | 0.000061 | 0.001152 | 358 | 1933 | 4 | 0 |
| 303 | 2184D | -310 | -334 | -344 | -409 | -604 | -357 | -616 | -45 | 0.0297662 | 0.0875734 | 0.1232877 | 0.0275510 | 6.7 | 6.6 | 6.5 | 4.3 | 6.5 | 4.2 | 9.4 | 0.000880 | 0.0008770 | 0.000845 | 0.000234 | 0.000856 | 0.000223 | 0.001538 | 7.1 | 7.0 | 6.7 | 4.3 | 6.9 | 4.2 | 10.3 | 0.000660 | 0.0006560 | 0.000640 | 0.000105 | 0.000639 | 0.000097 | 0.001223 | 620 | 1307 | 2 | 2 |
| 304 | 41B20 | -215 | -161 | -140 | -13 | -403 | -149 | -379 | 0 | 0.0730250 | 0.2074364 | 0.3463796 | 0.1437202 | 8.8 | 8.8 | 8.7 | 7.3 | 8.7 | 7.2 | 10.5 | 0.000623 | 0.0006140 | 0.000601 | 0.000360 | 0.000605 | 0.000345 | 0.000917 | 9.5 | 9.4 | 9.3 | 7.7 | 9.4 | 7.6 | 11.5 | 0.000450 | 0.0004440 | 0.000428 | 0.000211 | 0.000436 | 0.000202 | 0.000709 | 430 | 1813 | 5 | 1 |
| 305 | B1757 | -156 | -153 | -151 | -47 | -304 | -159 | -299 | -6 | 0.0153466 | 0.0264188 | 0.0410959 | 0.0079590 | 6.0 | 5.9 | 5.7 | 4.6 | 5.9 | 4.6 | 7.6 | 0.001092 | 0.0010820 | 0.001056 | 0.000617 | 0.001067 | 0.000597 | 0.001596 | 6.2 | 6.1 | 6.0 | 4.7 | 6.1 | 4.6 | 8.0 | 0.000764 | 0.0007590 | 0.000759 | 0.000336 | 0.000744 | 0.000326 | 0.001213 | 313 | 2925 | 5 | 4 |
| 306 | C6F06 | -282 | -241 | -227 | -153 | -530 | -238 | -501 | -1 | 0.0668452 | 0.1658513 | 0.2407045 | 0.0819105 | 7.4 | 7.3 | 6.9 | 5.4 | 7.2 | 5.3 | 9.7 | 0.001330 | 0.0013130 | 0.001272 | 0.000774 | 0.001295 | 0.000752 | 0.001944 | 8.0 | 7.8 | 7.5 | 5.8 | 7.8 | 5.6 | 10.6 | 0.000735 | 0.0007290 | 0.000723 | 0.000270 | 0.000714 | 0.000258 | 0.001214 | 564 | 1825 | 4 | 3 |
| 307 | D0701 | -100 | -92 | -88 | -14 | -193 | -93 | -187 | -2 | 0.0235864 | 0.0606654 | 0.1154599 | 0.0205242 | 8.2 | 8.2 | 8.1 | 7.0 | 8.1 | 7.0 | 9.5 | 0.000612 | 0.0006060 | 0.000592 | 0.000329 | 0.000597 | 0.000316 | 0.000920 | 8.9 | 8.8 | 8.8 | 7.5 | 8.8 | 7.5 | 10.3 | 0.000380 | 0.0003800 | 0.000373 | 0.000016 | 0.000369 | 0.000014 | 0.000742 | 199 | 3228 | 6 | 0 |